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: "Case-Based Planning to Learn"
  Abstract: Learning can be viewed as a problem of planning a series of modifications to memory. We adopt this view of learning and propose the applicability of the case-based planning methodology to the task of planning to learn. We argue that relatively simple, fine-grained primitive inferential operators are needed to support flexible planning. We show that it is possible to obtain the benefits of case-based reasoning within a planning to learn framework.

Connected through hyperedge HE-1 (induced by source paper "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"):
  with members:
    - "Opportunistic Reasoning: A Design Perspective"
    - "A theory of questions and question asking"
    - "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"
    - "A Functional Theory of Creative Reading"
    - "an Opportunistic Enterprise"
    - "A Comparative Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems"
    - "A Goal-Based Approach to Intelligent Information Retrieval"

Connected through hyperedge HE-2 (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"
    - "Towards A Computer Model of Memory Search Strategy Learning"
    - "Evaluating Computational Assistance for Crisis Response"

Connected through hyperedge HE-3 (induced by source paper "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A theory of questions and question asking"
    - "In Machine Learning: A Multistrategy Approach, Vol. IV  Macro and Micro Perspectives of Multistrategy Learning"
    - "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"
    - "Issues in Goal-Driven Explanation"
    - "Context-Based Similarity Applied to Retrieval of Relevant Cases"
    - "Hybrid Learning Using Genetic Algorithms and Decision Trees for Pattern Classification"
    - "Machine Learning and Inference"

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