human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Sifting informative examples from a random source."):
  Abstract: We discuss two types of algorithms for selecting relevant examples that have been developed in the context of computation learning theory. The examples are selected out of a stream of examples that are generated independently at random. The first two algorithms are the so-called "boosting" algorithms of Schapire [ Schapire, 1990 ] and Fre-und [ Freund, 1990 ] , and the Query-by-Committee algorithm of Seung [ Seung et al., 1992 ] . We describe the algorithms and some of their proven properties, point to some of their commonalities, and suggest some possible future implications.

Includes members:
  Member "Boosting a weak learning algorithm by majority To be published in Information and Computation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Efficient Distribution-free Learning of Probabilistic Concepts")
  Member "A General Lower Bound on the Number of Examples Needed for Learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Inductive Logic Programming")
  Member "Query by Committee":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Active Learning with Committees for Text Categorization")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) belongs to. The 7 classes are: Neural_Networks, Reinforcement_Learning, Probabilistic_Methods, Theory, Genetic_Algorithms, Case_Based, Rule_Learning. Directly output the class name.

gpt: Theory