  Learning a hidden hypergraph is a natural generalization of the classical
group testing problem that consists in detecting unknown hypergraph
$H_{un}=H(V,E)$ by carrying out edge-detecting tests. In the given paper we
focus our attention only on a specific family $\mathcal{F}(t,s,\ell)$ of
localized hypergraphs for which the total number of vertices $|V| = t$, the
number of edges $|E|\le s$, $s\ll t$, and the cardinality of any edge
$|e|\le\ell$, $\ell\ll t$. Our goal is to identify all edges of $H_{un}\in
\mathcal{F}(t,s,\ell)$ by using the minimal number of tests. We provide an
adaptive algorithm that matches the information theory bound, i.e., the total
number of tests of the algorithm in the worst case is at most $s\ell\log_2
t(1+o(1))$.
