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 "PAC-Learning PROLOG clauses with or without errors"):
  Abstract: In a nutshell we can describe a generic ILP problem as following: given a set E of (positive and negative) examples of a target predicate, and some background knowledge B about the world (usually a logic program including facts and auxiliary predicates), the task is to find a logic program H (our hypothesis) such that all positive examples can be deduced from B and H, while no negative example can. In this paper we review some of the results achieved in this area and discuss the techniques used. Moreover we prove the following new results: * Predicates described by non-recursive, local clauses of at most k literals are PAC-learnable under any distribution. This generalizes a previous result that was valid only for constrained clauses. * Predicates that are described by k non-recursive local clauses are PAC-learnable under any distribution. This generalizes a previous result that was non construc tive and valid only under some class of distributions. Finally we introduce what we believe is the first theoretical framework for learning Prolog clauses in the presence of errors. To this purpose we introduce a new noise model, that we call the fixed attribute noise model, for learning propositional concepts over the Boolean domain. This new noise model can be of its own interest.

Includes members:
  Member "Learning in the Presence of Malicious Errors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A General Lower Bound on the Number of Examples Needed for Learning")
  Member "Pac Learning, Noise, and Geometry":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A General Lower Bound on the Number of Examples Needed for Learning")
  Member "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "A General Lower Bound on the Number of Examples Needed for Learning")

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