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 "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.

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 "Towards A Computer Model of Memory Search Strategy Learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Dynamic Constraint Satisfaction using Case-Based Reasoning Techniques")
  Member "BECOMING AN EXPERT CASE-BASED REASONER: LEARNING TO ADAPT PRIOR CASES":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Goal-Driven Learning: Fundamental Issues (A Symposium Report)")

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