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: "The Role of Generic Models in Conceptual Change"
  Abstract: 1 This research was funded in part by NSF Grant No. IRI-92-10925 and in part by ONR Grant No. N00014-92-J-1234. We thank John Clement for the use of his protocol transcript, James Greeno for his contribution to developing our constructive modeling interpretation of it, and Ryan Tweney for his helpful comments Todd W. Griffith, Nancy J. Nersessian, and Ashok Goel Abstract We hypothesize generic models to be central in conceptual change in science. This hypothesis has its origins in two theoretical sources. The first source, constructive modeling, derives from a philosophical theory that synthesizes analyses of historical conceptual changes in science with investigations of reasoning and representation in cognitive psychology. The theory of constructive modeling posits generic mental models as productive in conceptual change. The second source, adaptive modeling, derives from a computational theory of creative design. Both theories posit situation independent domain abstractions, i.e. generic models. Using a constructive modeling interpretation of the reasoning exhibited in protocols collected by John Clement (1989) of a problem solving session involving conceptual change, we employ the resources of the theory of adaptive modeling to develop a new computational model, ToRQUE. Here we describe a piece of our analysis of the protocol to illustrate how our synthesis of the two theories is being used to develop a system for articulating and testing ToRQUE. The results of our research show how generic modeling plays a central role in conceptual change. They also demonstrate how such an interdisciplinary synthesis can provide significant insights into scientific reasoning.

Connected through hyperedge HE-1 (induced by source paper "The Structure-Mapping Engine: Algorithm and Examples"):
  with members:
    - "Utilizing Prior Concepts for Learning"
    - "The Case for Graph-Structured Representations"
    - "MAC/FAC: A Model of Similarity-based Retrieval"
    - "Adapting Abstract Knowledge"
    - "Role of Stories 1"
    - "Is analogical problem solving always analogical? The case for imitation. Second draft Is analogical problem"
    - "Functional Programming by Analogy"
    - "Modeling Analogical Problem Solving in a Production System Architecture"

Connected through hyperedge HE-2 (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"
    - "Learning Generic Mechanisms from Experiences for Analogical Reasoning"
    - "Discovery of Physical Principles from Design Experiences"

Connected through hyperedge HE-3 (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"
    - "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