human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "Concept Learning and the Problem of Small"):
  with members:
    - "Unifying Empirical and Explanation-Based Learning by Modeling the Utility of Learned Knowledge"
    - "Fossil: A Robust Relational Learner"
    - "The RISE System: Conquering Without Separating"
    - "Learning with Rare Cases and Small Disjuncts"
    - "The Problem with Noise and Small Disjuncts"
    - "On Learning Multiple Descriptions of a Concept"
    - "Using Partitioning to Speed Up Specific-to-General Rule Induction"

Question: Please tell me which class the center node 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