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: "Representing Self-knowledge for Introspection about Memory Search"
  Abstract: This position paper sketches a framework for modeling introspective reasoning and discusses the relevance of that framework for modeling introspective reasoning about memory search. It argues that effective and flexible memory processing in rich memories should be built on five types of explicitly represented self-knowledge: knowledge about information needs, relationships between different types of information, expectations for the actual behavior of the information search process, desires for its ideal behavior, and representations of how those expectations and desires relate to its actual performance. This approach to modeling memory search is both an illustration of general principles for modeling introspective reasoning and a step towards addressing the problem of how a reasoner human or machinecan acquire knowledge about the properties of its own knowledge base.

Connected through hyperedge HE-1 (induced by source paper "Abstract"):
  with members:
    - "Modeling Case-based Planning for Repairing Reasoning Failures"
    - "Introspective reasoning using meta-explanations for multistrategy learning"
    - "Using Knowledge of Cognitive Behavior to Learn from Failure"

Connected through hyperedge HE-2 (induced by source paper "Abstract"):
  with members:
    - "Modeling Case-based Planning for Repairing Reasoning Failures"

Connected through hyperedge HE-3 (induced by source paper "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"):
  with members:
    - "Towards Improving Case Adaptability with a Genetic Algorithm"
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "Combining Rules and Cases to Learn Case Adaptation"
    - "Towards A Computer Model of Memory Search Strategy Learning"
    - "Supporting Combined Human and Machine Planning: An Interface for Planning by Analogical Reasoning"

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