In reality, many real-world networks interact with and depend on other networks. We develop an analytical framework for studying interacting networks and present an exact percolation law for a network of $n$ interdependent networks (NON). We present a general framework to study the dynamics of the cascading failures process at each step caused by an initial failure occurring in the NON system. We study and compare both $n$ coupled Erdős-Rényi (ER) graphs and $n$ coupled random regular (RR) graphs. We found recently [Gao et. al. arXive:1010.5829] that for an NON composed of $n$ ER networks each of average degree $k$, the giant component, $P_{\infty}$, is given by $P_{\infty}=p[1-\exp(-kP_{\infty})]^n$ where $1-p$ is the initial fraction of removed nodes. Our general result coincides for $n=1$ with the known Erdős-Rényi second-order phase transition at a threshold, $p=p_c$, for a single network. For $n=2$ the general result for $P_{\infty}$ corresponds to the $n=2$ result [Buldyrev et. al., Nature, 464, (2010)]. Similar to the ER NON, for $n=1$ the percolation transition at $p_c$, is of second order while for any $n>1$ it is of first order. The first order percolation transition in both ER and RR (for $n>1$) is accompanied by cascading failures between the networks due to their interdependencies. However, we find that the robustness of $n$ coupled RR networks of degree $k$ is dramatically higher compared to the $n$ coupled ER networks of average degree $k$. While for ER NON there exists a critical minimum average degree $k=k_{\min}$, that increases with $n$, below which the system collapses, there is no such analogous $k_{\min}$ for RR NON system.