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: "Learning Generic Mechanisms from Experiences for Analogical Reasoning"
  Abstract: Humans appear to often solve problems in a new domain by transferring their expertise from a more familiar domain. However, making such cross-domain analogies is hard and often requires abstractions common to the source and target domains. Recent work in case-based design suggests that generic mechanisms are one type of abstractions used by designers. However, one important yet unexplored issue is where these generic mechanisms come from. We hypothesize that they are acquired incrementally from problem-solving experiences in familiar domains by generalization over patterns of regularity. Three important issues in generalization from experiences are what to generalize from an experience, how far to generalize, and what methods to use. In this paper, we show that mental models in a familiar domain provide the content, and together with the problem-solving context in which learning occurs, also provide the constraints for learning generic mechanisms from design experiences. In particular, we show how the model-based learning method integrated with similarity-based learning addresses the issues in generalization from experiences.

Connected through hyperedge HE-1 (induced by source paper "Functional Representation as Design Rationale"):
  with members:
    - "KRITIK: AN EARLY CASE-BASED DESIGN SYSTEM"
    - "Model-Based Learning of Structural Indices to Design Cases"
    - "EXPLANATORY INTERFACE IN INTERACTIVE DESIGN ENVIRONMENTS"

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"
    - "Generic Teleological Mechanisms and their Use in Case Adaptation"

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

Connected through hyperedge HE-4 (induced by source paper "an Opportunistic Enterprise"):
  with members:
    - "Opportunistic Reasoning: A Design Perspective"
    - "CASE-BASED CREATIVE DESIGN"
    - "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"

Connected through hyperedge HE-5 (induced by source paper "Generic Teleological Mechanisms and their Use in Case Adaptation"):
  with members:
    - "Modeling Case-based Planning for Repairing Reasoning Failures"
    - "A Model-Based Approach to Blame-Assignment in Design"
    - "KRITIK: AN EARLY CASE-BASED DESIGN SYSTEM"
    - "Model-Based Learning of Structural Indices to Design Cases"
    - "The Role of Generic Models in Conceptual Change"
    - "Discovery of Physical Principles from Design Experiences"

Connected through hyperedge HE-6 (induced by source paper "Design, Analogy, and Creativity"):
  with members:
    - "The Structure-Mapping Engine: Algorithm and Examples"

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