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 "Pessimistic and Optimistic Induction"):
  Abstract: Learning methods vary in the optimism or pessimism with which they regard the informativeness of learned knowledge. Pessimism is implicit in hypothesis testing, where we wish to draw cautious conclusions from experimental evidence. However, this paper demonstrates that optimism in the utility of derived rules may be the preferred bias for learning systems themselves. We examine the continuum between naive pessimism and naive optimism in the context of a decision tree learner that prunes rules based on stringent (i.e., pessimistic) or weak (i.e., optimistic) tests of their significance. Our experimental results indicate that in most cases optimism is preferred, but particularly in cases of sparse training data and high noise. This work generalizes earlier findings by Fisher and Schlimmer (1988) and Schaffer (1992), and we discuss its relevance to unsupervised learning, small disjuncts, and other issues.

Includes members:
  Member "Concept Learning and the Problem of Small":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Unifying Empirical and Explanation-Based Learning by Modeling the Utility of Learned Knowledge")

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