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: "Introspective reasoning using meta-explanations for multistrategy learning"
  Abstract: In order to learn effectively, a reasoner must not only possess knowledge about the world and be able to improve that knowledge, but it also must introspectively reason about how it performs a given task and what particular pieces of knowledge it needs to improve its performance at the current task. Introspection requires declarative representations of meta-knowledge of the reasoning performed by the system during the performance task, of the system's knowledge, and of the organization of this knowledge. This chapter presents a taxonomy of possible reasoning failures that can occur during a performance task, declarative representations of these failures, and associations between failures and particular learning strategies. The theory is based on Meta-XPs, which are explanation structures that help the system identify failure types, formulate learning goals, and choose appropriate learning strategies in order to avoid similar mistakes in the future. The theory is implemented in a computer model of an introspective reasoner that performs multistrategy learning during a story understanding task.

Connected through hyperedge HE-1 (induced by source paper "Integrating Creativity and Reading: A Functional Approach"):
  with members:
    - "A theory of questions and question asking"
    - "CASE-BASED CREATIVE DESIGN"
    - "Role of Ontology in Creative Understanding"

Connected through hyperedge HE-2 (induced by source paper "Abstract"):
  with members:
    - "Representing Self-knowledge for Introspection about Memory Search"
    - "Modeling Case-based Planning for Repairing Reasoning Failures"
    - "Using Knowledge of Cognitive Behavior to Learn from Failure"

Connected through hyperedge HE-3 (induced by source paper "Modeling Case-based Planning for Repairing Reasoning Failures"):
  with members:
    - "Abstract"
    - "Abstract"
    - "Using Knowledge of Cognitive Behavior to Learn from Failure"
    - "Abstract"
    - "Generic Teleological Mechanisms and their Use in Case Adaptation"
    - "Using Case-Based Reasoning for Mobile Robot Navigation"
    - "BECOMING AN EXPERT CASE-BASED REASONER: LEARNING TO ADAPT PRIOR CASES"

Connected through hyperedge HE-4 (induced by source paper "Role of Ontology in Creative Understanding"):
  with members:
    - "Integrating Creativity and Reading: A Functional Approach"
    - "CASE-BASED CREATIVE DESIGN"

Connected through hyperedge HE-5 (induced by source paper "Learning Problem-Solving Concepts by Reflecting on Problem Solving"):
  with members:
    - "Redesigning control knowledge of knowledge-based systems: machine learning meets knowledge engineering"

Connected through hyperedge HE-6 (induced by source paper "Abstract"):
  with members:
    - "A theory of questions and question asking"
    - "Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases"
    - "Choosing Learning Strategies to Achieve Learning Goals"

Connected through hyperedge HE-7 (induced by source paper "A Functional Theory of Creative Reading"):
  with members:
    - "A theory of questions and question asking"
    - "CASE-BASED CREATIVE DESIGN"
    - "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"

Connected through hyperedge HE-8 (induced by source paper "Towards A Computer Model of Memory Search Strategy Learning"):
  with members:
    - "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"
    - "Goal-Driven Learning: Fundamental Issues (A Symposium Report)"

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