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: "Model-Based Learning of Structural Indices to Design Cases"
  Abstract: A major issue in case-basedsystems is retrieving the appropriate cases from memory to solve a given problem. This implies that a case should be indexed appropriately when stored in memory. A case-based system, being dynamic in that it stores cases for reuse, needs to learn indices for the new knowledge as the system designers cannot envision that knowledge. Irrespective of the type of indexing (structural or functional), 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 structure-behavior-function (SBF) models help in learning structural indices to design cases in the domain 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 provides both the vocabulary for structural indexing of design cases and the inductive biases for index generalization. We further discuss how model-based learning can be integrated with similarity-based learning (that uses prior design cases) for learning the level of index generalization.

Connected through hyperedge HE-1 (induced by source paper "Functional Representation as Design Rationale"):
  with members:
    - "KRITIK: AN EARLY CASE-BASED DESIGN SYSTEM"
    - "Learning Generic Mechanisms from Experiences for Analogical Reasoning"
    - "EXPLANATORY INTERFACE IN INTERACTIVE DESIGN ENVIRONMENTS"

Connected through hyperedge HE-2 (induced by source paper "Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases"):
  with members:
    - "A theory of questions and question asking"
    - "Using Introspective Reasoning to Select Learning Strategies"
    - "Abstract"
    - "Learning Indices for Schema Selection"

Connected through hyperedge HE-3 (induced by source paper "Use of Mental Models for Constraining Index Learning in Experience-Based Design"):
  with members:
    - "KRITIK: AN EARLY CASE-BASED DESIGN SYSTEM"
    - "Discovery of Physical Principles from Design Experiences"

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

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