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: "Abstract"
  Abstract: Metacognition addresses the issues of knowledge about cognition and regulating cognition. We argue that the regulation process should be improved with growing experience. Therefore mental models are needed which facilitate the re-use of previous regulation processes. We will satisfy this requirement by describing a case-based approach to Introspection Planning which utilises previous experience obtained during reasoning at the meta-level and at the object level. The introspection plans used in this approach support various metacognitive tasks which are identified by the generation of self-questions. As an example of introspection planning, the metacognitive behaviour of our system, IULIAN, is described.

Connected through hyperedge HE-1 (induced by source paper "Representing Self-knowledge for Introspection about Memory Search"):
  with members:
    - "Abstract"
    - "Abstract"
    - "Learning to Improve Case Adaptation by Introspective Reasoning and CBR"

Connected through hyperedge HE-2 (induced by source paper "Modeling Case-based Planning for Repairing Reasoning Failures"):
  with members:
    - "Abstract"
    - "Introspective reasoning using meta-explanations for multistrategy learning"
    - "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-3 (induced by source paper "Using Knowledge of Cognitive Behavior to Learn from Failure"):
  with members:
    - "Modeling Case-based Planning for Repairing Reasoning Failures"

Connected through hyperedge HE-4 (induced by source paper "Introspective reasoning using meta-explanations for multistrategy learning"):
  with members:
    - "Modeling Case-based Planning for Repairing Reasoning Failures"
    - "Integrating Creativity and Reading: A Functional Approach"
    - "Role of Ontology in Creative Understanding"
    - "Abstract"
    - "A Functional Theory of Creative Reading"
    - "Towards A Computer Model of Memory Search Strategy Learning"
    - "Issues in Goal-Driven Explanation"
    - "Learning Problem-Solving Concepts by Reflecting on Problem Solving"

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