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: "Learning Adaptation Strategies by Introspective Reasoning about Memory Search"
  Abstract: In case-based reasoning systems, the case adaptation process is traditionally controlled by static libraries of hand-coded adaptation rules. This paper proposes a method for learning adaptation knowledge in the form of adaptation strategies of the type developed and hand-coded by Kass [90] . Adaptation strategies differ from standard adaptation rules in that they encode general memory search procedures for finding the information needed during case adaptation; this paper focuses on the issues involved in learning memory search procedures to form the basis of new adaptation strategies. It proposes a method that starts with a small library of abstract adaptation rules and uses introspective reasoning about the system's memory organization to generate the memory search plans needed to apply those rules. The search plans are then packaged with the original abstract rules to form new adaptation strategies for future use. This process allows a CBR system not only to learn about its domain, by storing the results of case adaptation, but also to learn how to apply the cases in its memory more effectively.

Connected through hyperedge HE-1 (induced by source paper "BECOMING AN EXPERT CASE-BASED REASONER: LEARNING TO ADAPT PRIOR CASES"):
  with members:
    - "Modeling Case-based Planning for Repairing Reasoning Failures"
    - "Towards A Computer Model of Memory Search Strategy Learning"
    - "Goal-Driven Learning: Fundamental Issues (A Symposium Report)"

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"
    - "Abstract"
    - "A Functional Theory of Creative Reading"
    - "Towards A Computer Model of Memory Search Strategy Learning"
    - "Issues in Goal-Driven Explanation"

Connected through hyperedge HE-3 (induced by source paper "Towards A Computer Model of Memory Search Strategy Learning"):
  with members:
    - "Introspective reasoning using meta-explanations for multistrategy learning"
    - "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "Combining Rules and Cases to Learn Case Adaptation"
    - "BECOMING AN EXPERT CASE-BASED REASONER: LEARNING TO ADAPT PRIOR CASES"
    - "Dynamic Constraint Satisfaction using Case-Based Reasoning Techniques"
    - "Goal-Driven Learning: Fundamental Issues (A Symposium Report)"

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