human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Use of Mental Models for Constraining Index Learning in Experience-Based Design"):
  Abstract: The power of the case-based method comes from the ability to retrieve the "right" case when a new problem is specified. This implies that learning the "right" indices to a case before storing it for potential reuse is crucial for the success of the method. A hierarchical organization of the case memory raises two distinct but related issues in index learning: learning the indexing vocabulary, and learning the right level of generalization. In this paper we show how the use of structure-behavior-function (SBF) models constrains index learning in the context of experience-based design of physical devices. The SBF model of a design provides the functional and causal explanation of how the structure of the design delivers its function. We describe how the SBF model of a design, together with a specification of the task for which the design case might be reused, provides the vocabulary for indexing the design case in memory. We also discuss how the prior design experiences stored in case-memory help to determine the level of index generalization. The KRITIK2 system implements and evaluates the model-based method for learning indices to design cases.

Includes members:
  Member "KRITIK: AN EARLY CASE-BASED DESIGN SYSTEM":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Functional Representation as Design Rationale")
  Member "Model-Based Learning of Structural Indices to Design Cases":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Functional Representation as Design Rationale")
  Member "Discovery of Physical Principles from Design Experiences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Generic Teleological Mechanisms and their Use in Case Adaptation")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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