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: "Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases"
  Abstract: This article describes how a reasoner can improve its understanding of an incompletely understood domain through the application of what it already knows to novel problems in that domain. Case-based reasoning is the process of using past experiences stored in the reasoner's memory to understand novel situations or solve novel problems. However, this process assumes that past experiences are well understood and provide good "lessons" to be used for future situations. This assumption is usually false when one is learning about a novel domain, since situations encountered previously in this domain might not have been understood completely. Furthermore, the reasoner may not even have a case that adequately deals with the new situation, or may not be able to access the case using existing indices. We present a theory of incremental learning based on the revision of previously existing case knowledge in response to experiences in such situations. The theory has been implemented in a case-based story understanding program that can (a) learn a new case in situations where no case already exists, (b) learn how to index the case in memory, and (c) incrementally refine its understanding of the case by using it to reason about new situations, thus evolving a better understanding of its domain through experience. This research complements work in case-based reasoning by providing mechanisms by which a case library can be automatically built for use by a case-based reasoning program.

Connected through hyperedge HE-1 (induced by source paper "Using Introspective Reasoning to Select Learning Strategies"):
  with members:
    - "Abstract"
    - "Decision Models: A Theory of Volitional Explanation"
    - "Incremental Learning of Explanation Patterns and their Indices"

Connected through hyperedge HE-2 (induced by source paper "Learning Indices for Schema Selection"):
  with members:
    - "Decision Models: A Theory of Volitional Explanation"
    - "Incremental Learning of Explanation Patterns and their Indices"

Connected through hyperedge HE-3 (induced by source paper "Abstract"):
  with members:
    - "Introspective reasoning using meta-explanations for multistrategy learning"
    - "A theory of questions and question asking"
    - "Choosing Learning Strategies to Achieve Learning Goals"

Connected through hyperedge HE-4 (induced by source paper "A theory of questions and question asking"):
  with members:
    - "Integrating Creativity and Reading: A Functional Approach"
    - "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"
    - "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"
    - "Decision Models: A Theory of Volitional Explanation"
    - "Incremental Learning of Explanation Patterns and their Indices"
    - "Abstract"
    - "A Functional Theory of Creative Reading"

Connected through hyperedge HE-5 (induced by source paper "Model-Based Learning of Structural Indices to Design Cases"):
  with members:
    - "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"
    - "Functional Representation as Design Rationale"

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