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 Functional Theory of Creative Reading"
  Abstract: Reading is an area of human cognition which has been studied for decades by psychologists, education researchers, and artificial intelligence researchers. Yet, there still does not exist a theory which accurately describes the complete process. We believe that these past attempts fell short due to an incomplete understanding of the overall task of reading; namely, the complete set of mental tasks a reasoner must perform to read and the mechanisms that carry out these tasks. We present a functional theory of the reading process and argue that it represents a coverage of the task. The theory combines experimental results from psychology, artificial intelligence, education, and linguistics, along with the insights we have gained from our own research. This greater understanding of the mental tasks necessary for reading will enable new natural language understanding systems to be more flexible and more capable than earlier ones. Furthermore, we argue that creativity is a necessary component of the reading process and must be considered in any theory or system attempting to describe it. We present a functional theory of creative reading and a novel knowledge organization scheme that supports the creativity mechanisms. The reading theory is currently being implemented in the ISAAC (Integrated Story Analysis And Creativity) system, a computer system which reads science fiction stories. fl This paper is part of the Georgia Institute of Technology, College of Computing, Technical Report series.

Connected through hyperedge HE-1 (induced by source paper "CASE-BASED CREATIVE DESIGN"):
  with members:
    - "Understanding Creativity: A Case-Based Approach"
    - "Opportunistic Reasoning: A Design Perspective"
    - "Explaining Serendipitous Recognition in Design"
    - "Finding Analogues for Innovative Design"
    - "Integrating Creativity and Reading: A Functional Approach"
    - "Role of Ontology in Creative Understanding"
    - "an Opportunistic Enterprise"

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"
    - "an Opportunistic Enterprise"
    - "A Comparative Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems"
    - "Case-Based Planning to Learn"
    - "A Goal-Based Approach to Intelligent Information Retrieval"

Connected through hyperedge HE-3 (induced by source paper "Introspective reasoning using meta-explanations for multistrategy learning"):
  with members:
    - "Modeling Case-based Planning for Repairing Reasoning Failures"
    - "Abstract"
    - "Integrating Creativity and Reading: A Functional Approach"
    - "Role of Ontology in Creative Understanding"
    - "Abstract"
    - "Towards A Computer Model of Memory Search Strategy Learning"
    - "Issues in Goal-Driven Explanation"
    - "Learning Problem-Solving Concepts by Reflecting on Problem Solving"

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

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