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: "Generic Teleological Mechanisms and their Use in Case Adaptation"
  Abstract: In experience-based (or case-based) reasoning, new problems are solved by retrieving and adapting the solutions to similar problems encountered in the past. An important issue in experience-based reasoning is to identify different types of knowledge and reasoning useful for different classes of case-adaptation tasks. In this paper, we examine a class of non-routine case-adaptation tasks that involve patterned insertions of new elements in old solutions. We describe a model-based method for solving this task in the context of the design of physical devices. The method uses knowledge of generic teleological mechanisms (GTMs) such as cascading. Old designs are adapted to meet new functional specifications by accessing and instantiating the appropriate GTM. The Kritik2 system evaluates the computational feasibility and sufficiency of this method for design adaptation.

Connected through hyperedge HE-1 (induced by source paper "Modeling Case-based Planning for Repairing Reasoning Failures"):
  with members:
    - "Abstract"
    - "Abstract"
    - "Introspective reasoning using meta-explanations for multistrategy learning"
    - "Using Knowledge of Cognitive Behavior to Learn from Failure"
    - "Abstract"
    - "Using Case-Based Reasoning for Mobile Robot Navigation"
    - "BECOMING AN EXPERT CASE-BASED REASONER: LEARNING TO ADAPT PRIOR CASES"

Connected through hyperedge HE-2 (induced by source paper "The Role of Generic Models in Conceptual Change"):
  with members:
    - "The Structure-Mapping Engine: Algorithm and Examples"
    - "Learning Generic Mechanisms from Experiences for Analogical Reasoning"

Connected through hyperedge HE-3 (induced by source paper "A Model-Based Approach to Blame-Assignment in Design"):
  with members:
    - "Meta-Cases: Explaining Case-Based Reasoning"
    - "METHOD-SPECIFIC KNOWLEDGE COMPILATION: TOWARDS PRACTICAL DESIGN SUPPORT SYSTEMS"
    - "KRITIK: AN EARLY CASE-BASED DESIGN SYSTEM"

Connected through hyperedge HE-4 (induced by source paper "Discovery of Physical Principles from Design Experiences"):
  with members:
    - "Model-Based Learning of Structural Indices to Design Cases"
    - "Learning Generic Mechanisms from Experiences for Analogical Reasoning"
    - "Use of Mental Models for Constraining Index Learning in Experience-Based Design"

Connected through hyperedge HE-5 (induced by source paper "KRITIK: AN EARLY CASE-BASED DESIGN SYSTEM"):
  with members:
    - "A Model-Based Approach to Blame-Assignment in Design"
    - "METHOD-SPECIFIC KNOWLEDGE COMPILATION: TOWARDS PRACTICAL DESIGN SUPPORT SYSTEMS"
    - "Use of Mental Models for Constraining Index Learning in Experience-Based Design"
    - "Functional Representation as Design Rationale"
    - "Modeling Invention by Analogy in ACT-R"

Connected through hyperedge HE-6 (induced by source paper "Model-Based Learning of Structural Indices to Design Cases"):
  with members:
    - "Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases"
    - "Use of Mental Models for Constraining Index Learning in Experience-Based Design"
    - "Discovery of Physical Principles from Design Experiences"
    - "Functional Representation as Design Rationale"

Connected through hyperedge HE-7 (induced by source paper "Learning Generic Mechanisms from Experiences for Analogical Reasoning"):
  with members:
    - "an Opportunistic Enterprise"
    - "The Role of Generic Models in Conceptual Change"
    - "Discovery of Physical Principles from Design Experiences"
    - "Functional Representation as Design Rationale"
    - "Design, Analogy, and Creativity"

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