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: "A Goal-Based Approach to Intelligent Information Retrieval"
  Abstract: Intelligent information retrieval (IIR) requires inference. The number of inferences that can be drawn by even a simple reasoner is very large, and the inferential resources available to any practical computer system are limited. This problem is one long faced by AI researchers. In this paper, we present a method used by two recent machine learning programs for control of inference that is relevant to the design of IIR systems. The key feature of the approach is the use of explicit representations of desired knowledge, which we call knowledge goals. Our theory addresses the representation of knowledge goals, methods for generating and transforming these goals, and heuristics for selecting among potential inferences in order to feasibly satisfy such goals. In this view, IIR becomes a kind of planning: decisions about what to infer, how to infer and when to infer are based on representations of desired knowledge, as well as internal representations of the system's inferential abilities and current state. The theory is illustrated using two case studies, a natural language understanding program that learns by reading novel newspaper stories, and a differential diagnosis program that improves its accuracy with experience. We conclude by making several suggestions on how this machine learning framework can be integrated with existing information retrieval methods.

Connected through hyperedge HE-1 (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"
    - "Decision Models: A Theory of Volitional Explanation"
    - "Learning Indices for Schema Selection"

Connected through hyperedge HE-2 (induced by source paper "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"):
  with members:
    - "Opportunistic Reasoning: A Design Perspective"
    - "A theory of questions and question asking"
    - "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"
    - "A Functional Theory of Creative Reading"
    - "an Opportunistic Enterprise"
    - "A Comparative Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems"
    - "Case-Based Planning to Learn"

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