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 "Abstract"):
  Abstract: Self-selection of input examples on the basis of performance failure is a powerful bias for learning systems. The definition of what constitutes a learning bias, however, has been typically restricted to bias provided by the input language, hypothesis language, and preference criteria between competing concept hypotheses. But if bias is taken in the broader context as any basis that provides a preference for one concept change over another, then the paradigm of failure-driven processing indeed provides a bias. Bias is exhibited by the selection of examples from an input stream that are examples of failure; successful performance is filtered out. We show that the degrees of freedom are less in failure-driven learning than in success-driven learning and that learning is facilitated because of this constraint. We also broaden the definition of failure, provide a novel taxonomy of failure causes, and illustrate the interaction of both in a multistrategy learning system called Meta-AQUA.

Includes members:
  Member "Introspective reasoning using meta-explanations for multistrategy learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Integrating Creativity and Reading: A Functional Approach")
  Member "A theory of questions and question asking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Integrating Creativity and Reading: A Functional Approach")
  Member "Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Using Introspective Reasoning to Select Learning Strategies")
  Member "Choosing Learning Strategies to Achieve Learning Goals":
    also connected through other hyperedges:
      (none)

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: Case_Based