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 "BECOMING AN EXPERT CASE-BASED REASONER: LEARNING TO ADAPT PRIOR CASES"):
  Abstract: Experience plays an important role in the development of human expertise. One computational model of how experience affects expertise is provided by research on case-based reasoning, which examines how stored cases encapsulating traces of specific prior problem-solving episodes can be retrieved and re-applied to facilitate new problem-solving. Much progress has been made in methods for accessing relevant cases, and case-based reasoning is receiving wide acceptance both as a technology for developing intelligent systems and as a cognitive model of a human reasoning process. However, one important aspect of case-based reasoning remains poorly understood: the process by which retrieved cases are adapted to fit new situations. The difficulty of encoding effective adaptation rules by hand is widely recognized as a serious impediment to the development of fully autonomous case-based reasoning systems. Consequently, an important question is how case-based reasoning systems might learn to improve their expertise at case adaptation. We present a framework for acquiring this expertise by using a combination of general adaptation rules, introspective reasoning, and case-based reasoning about the case adaptation task itself.

Includes members:
  Member "Modeling Case-based Planning for Repairing Reasoning Failures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Abstract")
  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 "Learning Adaptation Strategies by Introspective Reasoning about Memory Search":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Introspective reasoning using meta-explanations for multistrategy learning")
  Member "Goal-Driven Learning: Fundamental Issues (A Symposium Report)":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Towards A Computer Model of Memory Search Strategy Learning")

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