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: "Functional Representation as Design Rationale"
  Abstract: Design rationale is a record of design activity: of alternatives available, choices made, the reasons for them, and explanations of how a proposed design is intended to work. We describe a representation called the Functional Representation (FR) that has been used to represent how a device's functions arise causally from the functions of its components and their interconnections. We propose that FR can provide the basis for capturing the causal aspects of the design rationale. We briefly discuss the use of FR for a number of tasks in which we would expect the design rationale to be useful: generation of diagnostic knowledge, design verification and redesign.

Connected through hyperedge HE-1 (induced by source paper "EXPLANATORY INTERFACE IN INTERACTIVE DESIGN ENVIRONMENTS"):
  with members:
    (none)

Connected through hyperedge HE-2 (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"
    - "Generic Teleological Mechanisms and their Use in Case Adaptation"
    - "Use of Mental Models for Constraining Index Learning in Experience-Based Design"
    - "Modeling Invention by Analogy in ACT-R"

Connected through hyperedge HE-3 (induced by source paper "Model-Based Learning of Structural Indices to Design Cases"):
  with members:
    - "Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases"
    - "Generic Teleological Mechanisms and their Use in Case Adaptation"
    - "Use of Mental Models for Constraining Index Learning in Experience-Based Design"
    - "Discovery of Physical Principles from Design Experiences"

Connected through hyperedge HE-4 (induced by source paper "Learning Generic Mechanisms from Experiences for Analogical Reasoning"):
  with members:
    - "an Opportunistic Enterprise"
    - "Generic Teleological Mechanisms and their Use in Case Adaptation"
    - "The Role of Generic Models in Conceptual Change"
    - "Discovery of Physical Principles from Design Experiences"
    - "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