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: "Goal-Driven Learning: Fundamental Issues (A Symposium Report)"
  Abstract: In Artificial Intelligence, Psychology, and Education, a growing body of research supports the view that learning is a goal-directed process. Psychological experiments show that people with different goals process information differently; studies in education show that goals have strong effects on what students learn; and functional arguments from machine learning support the necessity of goal-based focusing of learner effort. At the Fourteenth Annual Conference of the Cognitive Science Society, a symposium brought together researchers in AI, psychology, and education to discuss goal-driven learning. This article presents the fundamental points illuminated by the symposium, placing them in the context of open questions and current research di rections in goal-driven learning. fl Appears in AI Magazine, 14(4):67-72, 1993

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"
    - "Learning Adaptation Strategies by Introspective Reasoning about Memory Search"

Connected through hyperedge HE-2 (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"
    - "Learning Adaptation Strategies by Introspective Reasoning about Memory Search"
    - "BECOMING AN EXPERT CASE-BASED REASONER: LEARNING TO ADAPT PRIOR CASES"
    - "Dynamic Constraint Satisfaction using Case-Based Reasoning Techniques"

Connected through hyperedge HE-3 (induced by source paper "Issues in Goal-Driven Explanation"):
  with members:
    - "Introspective reasoning using meta-explanations for multistrategy learning"
    - "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"
    - "AN ARCHITECTURE FOR GOAL-DRIVEN EXPLANATION"

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