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: "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.

Connected through hyperedge HE-1 (induced by source paper "Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases"):
  with members:
    - "A theory of questions and question asking"
    - "Using Introspective Reasoning to Select Learning Strategies"
    - "Model-Based Learning of Structural Indices to Design Cases"
    - "Learning Indices for Schema Selection"

Connected through hyperedge HE-2 (induced by source paper "Introspective reasoning using meta-explanations for multistrategy learning"):
  with members:
    - "Modeling Case-based Planning for Repairing Reasoning Failures"
    - "Abstract"
    - "Integrating Creativity and Reading: A Functional Approach"
    - "Role of Ontology in Creative Understanding"
    - "A Functional Theory of Creative Reading"
    - "Towards A Computer Model of Memory Search Strategy Learning"
    - "Issues in Goal-Driven Explanation"
    - "Learning Problem-Solving Concepts by Reflecting on Problem Solving"

Connected through hyperedge HE-3 (induced by source paper "Choosing Learning Strategies to Achieve Learning Goals"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "A theory of questions and question asking"):
  with members:
    - "Integrating Creativity and Reading: A Functional Approach"
    - "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"
    - "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"
    - "Decision Models: A Theory of Volitional Explanation"
    - "Incremental Learning of Explanation Patterns and their Indices"
    - "Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases"
    - "A Functional Theory of Creative Reading"

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: Case_Based