  We propose a hypergraph-based active learning scheme which we term $HS^2$,
$HS^2$ generalizes the previously reported algorithm $S^2$ originally proposed
for graph-based active learning with pointwise queries [Dasarathy et al., COLT
2015]. Our $HS^2$ method can accommodate hypergraph structures and allows one
to ask both pointwise queries and pairwise queries. Based on a novel parametric
system particularly designed for hypergraphs, we derive theoretical results on
the query complexity of $HS^2$ for the above described generalized settings.
Both the theoretical and empirical results show that $HS^2$ requires a
significantly fewer number of queries than $S^2$ when one uses $S^2$ over a
graph obtained from the corresponding hypergraph via clique expansion.
