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: "Incremental Learning of Explanation Patterns and their Indices"
  Abstract: This paper 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. Recent work in AI has dealt with the issue of using past explanations stored in the reasoner's memory to understand novel situations. However, this process assumes that past explanations 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. Instead, it is reasonable to assume that the reasoner would have gaps in its knowledge base. By reasoning about a new situation, the reasoner should be able to fill in these gaps as new information came in, reorganize its explanations in memory, and gradually evolve a better understanding of its domain. We present a story understanding program that retrieves past explanations from situations already in memory, and uses them to build explanations to understand novel stories about terrorism. In doing so, the system refines its understanding of the domain by filling in gaps in these explanations, by elaborating the explanations, or by learning new indices for the explanations. This is a type of incremental learning since the system improves its explanatory knowledge of the domain in an incremental fashion rather than by learning new XPs as a whole.

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"
    - "Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases"

Connected through hyperedge HE-2 (induced by source paper "Learning Indices for Schema Selection"):
  with members:
    - "Decision Models: A Theory of Volitional Explanation"
    - "Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases"

Connected through hyperedge HE-3 (induced by source paper "Decision Models: A Theory of Volitional Explanation"):
  with members:
    - "A theory of questions and question asking"
    - "Using Introspective Reasoning to Select Learning Strategies"
    - "Learning Indices for Schema Selection"

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"
    - "Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases"
    - "Abstract"
    - "A Functional Theory of Creative Reading"

Connected through hyperedge HE-5 (induced by source paper "A Goal-Based Approach to Intelligent Information Retrieval"):
  with members:
    - "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"

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