We construct coresets for the continuous $k$-center problem in fixed-dimensional hyperbolic space $\mathbb H^D$. The input is a set $P$ of $n$ points in $\mathbb H^D$, where $D=O(1)$, and the centers may be placed anywhere in the ambient hyperbolic space. Given $\varepsilon\in(0,1)$, we construct a subset $P_\varepsilon\subseteq P$ such that every optimal continuous $k$-center solution for $P_\varepsilon$ is a $(1+\varepsilon)$-approximation for $P$. The main difficulty is the exponential volume growth of hyperbolic balls, which prevents a direct grid-based coreset from having size independent of the input radius. We overcome this by dividing the construction according to the farthest-first scale. At bounded scales, we use local Euclidean grids in the Poincaré ball model. At intermediate scales, we use an anchor-centered shell--cone decomposition together with exact distance profiles obtained from the hyperbolic law of cosines. At large scales, we avoid discretizing the ambient ball and instead keep input witnesses indexed by coarse profiles of the induced $k$-center distance functions on each shell--cone bucket. The resulting coreset has size $\left(1/\varepsilon\right)^{O(kD)}$ and can be constructed in time $O(nk\left(1/\varepsilon\right)^{O(kD)}).$ Both bounds are independent of the input radius, and the coreset size is also independent of $n$. Consequently, for fixed $D$, $k$, and $\varepsilon$, this gives a linear-time construction of a constant-size coreset for the continuous $k$-center problem in hyperbolic space.