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: "Decision Models: A Theory of Volitional Explanation"
  Abstract: This paper presents a theory of motivational analysis, the construction of volitional explanations to describe the planning behavior of agents. We discuss both the content of such explanations, as well as the process by which an understander builds the explanations. Explanations are constructed from decision models, which describe the planning process that an agent goes through when considering whether to perform an action. Decision models are represented as explanation patterns, which are standard patterns of causality based on previous experiences of the understander. We discuss the nature of explanation patterns, their use in representing decision models, and the process by which they are retrieved, used and evaluated.

Connected through hyperedge HE-1 (induced by source paper "Using Introspective Reasoning to Select Learning Strategies"):
  with members:
    - "Abstract"
    - "Incremental Learning of Explanation Patterns and their Indices"
    - "Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases"

Connected through hyperedge HE-2 (induced by source paper "Incremental Learning of Explanation Patterns and their Indices"):
  with members:
    - "A theory of questions and question asking"
    - "Using Introspective Reasoning to Select Learning Strategies"
    - "Learning Indices for Schema Selection"
    - "A Goal-Based Approach to Intelligent Information Retrieval"

Connected through hyperedge HE-3 (induced by source paper "Learning Indices for Schema Selection"):
  with members:
    - "Incremental Learning of Explanation Patterns and their Indices"
    - "Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases"

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

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