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: "The Use of Explicit Goals for Knowledge to Guide Inference and Learning"
  Abstract: Combinatorial explosion of inferences has always been a central problem in artificial intelligence. Although the inferences that can be drawn from a reasoner's knowledge and from available inputs is very large (potentially infinite), the inferential resources available to any reasoning system are limited. With limited inferential capacity and very many potential inferences, reasoners must somehow control the process of inference. Not all inferences are equally useful to a given reasoning system. Any reasoning system that has goals (or any form of a utility function) and acts based on its beliefs indirectly assigns utility to its beliefs. Given limits on the process of inference, and variation in the utility of inferences, it is clear that a reasoner ought to draw the inferences that will be most valuable to it. This paper presents an approach to this problem that makes the utility of a (potential) belief an explicit part of the inference process. The method is to generate explicit desires for knowledge. The question of focus of attention is thereby transformed into two related problems: How can explicit desires for knowledge be used to control inference and facilitate resource-constrained goal pursuit in general? and, Where do these desires for knowledge come from? We present a theory of knowledge goals, or desires for knowledge, and their use in the processes of understanding and learning. The theory is illustrated using two case studies, a natural language understanding program that learns by reading novel or unusual newspaper stories, and a differential diagnosis program that improves its accuracy with experience.

Connected through hyperedge HE-1 (induced by source paper "A Comparative Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems"):
  with members:
    - "AN EMPIRICAL APPROACH TO SOLVING THE GENERAL UTILITY PROBLEM IN SPEEDUP LEARNING"
    - "Design and Implementation of a Replay Framework based on a Partial Order Planner"
    - "An Explanation-Based Approach to Improve Retrieval in Case-Based Planning"
    - "A utility-based approach to learning in a mixed Case-Based and Model-Based Reasoning architecture"

Connected through hyperedge HE-2 (induced by source paper "Opportunistic Reasoning: A Design Perspective"):
  with members:
    - "Towards More Creative Case-Based Design Systems"
    - "Explaining Serendipitous Recognition in Design"
    - "CASE-BASED CREATIVE DESIGN"
    - "an Opportunistic Enterprise"
    - "Modeling Invention by Analogy in ACT-R"

Connected through hyperedge HE-3 (induced by source paper "Case-Based Planning to Learn"):
  with members:
    - "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"
    - "Combining Rules and Cases to Learn Case Adaptation"

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"
    - "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"
    - "A Functional Theory of Creative Reading"

Connected through hyperedge HE-5 (induced by source paper "A Functional Theory of Creative Reading"):
  with members:
    - "Introspective reasoning using meta-explanations for multistrategy learning"
    - "A theory of questions and question asking"
    - "CASE-BASED CREATIVE DESIGN"

Connected through hyperedge HE-6 (induced by source paper "an Opportunistic Enterprise"):
  with members:
    - "Opportunistic Reasoning: A Design Perspective"
    - "CASE-BASED CREATIVE DESIGN"
    - "Learning Generic Mechanisms from Experiences for Analogical Reasoning"

Connected through hyperedge HE-7 (induced by source paper "A Goal-Based Approach to Intelligent Information Retrieval"):
  with members:
    - "Incremental Learning of Explanation Patterns and their Indices"

Connected through hyperedge HE-8 (induced by source paper "INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A theory of questions and question asking"
    - "In Machine Learning: A Multistrategy Approach, Vol. IV  Macro and Micro Perspectives of Multistrategy Learning"
    - "Issues in Goal-Driven Explanation"
    - "Context-Based Similarity Applied to Retrieval of Relevant Cases"
    - "Hybrid Learning Using Genetic Algorithms and Decision Trees for Pattern Classification"
    - "Machine Learning and Inference"
    - "Machine Learning and Inference"

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