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: "Towards A Computer Model of Memory Search Strategy Learning"
  Abstract: Much recent research on modeling memory processes has focused on identifying useful indices and retrieval strategies to support particular memory tasks. Another important question concerning memory processes, however, is how retrieval criteria are learned. This paper examines the issues involved in modeling the learning of memory search strategies. It discusses the general requirements for appropriate strategy learning and presents a model of memory search strategy learning applied to the problem of retrieving relevant information for adapting cases in case-based reasoning. It discusses an implementation of that model, and, based on the lessons learned from that implementation, points towards issues and directions in refining the model.

Connected through hyperedge HE-1 (induced by source paper "Dynamic Constraint Satisfaction using Case-Based Reasoning Techniques"):
  with members:
    - "Towards Improving Case Adaptability with a Genetic Algorithm"
    - "Synergy and Commonality in Case-Based and Constraint-Based Reasoning"
    - "A CBR Integration From Inception to Productization"

Connected through hyperedge HE-2 (induced by source paper "Goal-Driven Learning: Fundamental Issues (A Symposium Report)"):
  with members:
    - "Issues in Goal-Driven Explanation"
    - "BECOMING AN EXPERT CASE-BASED REASONER: LEARNING TO ADAPT PRIOR CASES"

Connected through hyperedge HE-3 (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"
    - "Learning Adaptation Strategies by Introspective Reasoning about Memory Search"
    - "Goal-Driven Learning: Fundamental Issues (A Symposium Report)"

Connected through hyperedge HE-4 (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"
    - "Issues in Goal-Driven Explanation"
    - "Learning Problem-Solving Concepts by Reflecting on Problem Solving"

Connected through hyperedge HE-5 (induced by source paper "Combining Rules and Cases to Learn Case Adaptation"):
  with members:
    - "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "Evaluating Computational Assistance for Crisis Response"
    - "Case-Based Planning to Learn"

Connected through hyperedge HE-6 (induced by source paper "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"):
  with members:
    - "Representing Self-knowledge for Introspection about Memory Search"
    - "Towards Improving Case Adaptability with a Genetic Algorithm"
    - "Acquiring Case Adaptation Knowledge: A Hybrid Approach"
    - "Combining Rules and Cases to Learn Case Adaptation"
    - "Supporting Combined Human and Machine Planning: An Interface for Planning by Analogical Reasoning"

Connected through hyperedge HE-7 (induced by source paper "Acquiring Case Adaptation Knowledge: A Hybrid Approach"):
  with members:
    - "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"
    - "Combining Rules and Cases to Learn Case Adaptation"
    - "Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning"
    - "Case-Based Similarity Assessment: Estimating Adaptability from Experience"
    - "A Case Study of Case-Based CBR"
    - "on Case-Based Reasoning Integrations Case-Based Seeding for an Interactive Crisis Response Assistant"

Connected through hyperedge HE-8 (induced by source paper "Learning Adaptation Strategies by Introspective Reasoning about Memory Search"):
  with members:
    - "Introspective reasoning using meta-explanations for multistrategy learning"
    - "BECOMING AN EXPERT CASE-BASED REASONER: LEARNING TO ADAPT PRIOR CASES"

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