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 Comparative Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems"
  Abstract: The utility problem in learning systems occurs when knowledge learned in an attempt to improve a system's performance degrades performance instead. We present a methodology for the analysis of utility problems which uses computational models of problem solving systems to isolate the root causes of a utility problem, to detect the threshold conditions under which the problem will arise, and to design strategies to eliminate it. We present models of case-based reasoning and control-rule learning systems and compare their performance with respect to the swamping utility problem. Our analysis suggests that case-based reasoning systems are more resistant to the utility problem than control-rule learning systems. 1

Connected through hyperedge HE-1 (induced by source paper "An Explanation-Based Approach to Improve Retrieval in Case-Based Planning"):
  with members:
    - "Evaluating the Effectiveness of Derivation Replay in Partial-order vs State-space Planning"
    - "Design and Implementation of a Replay Framework based on a Partial Order Planner"

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"
    - "Case-Based Planning to Learn"
    - "A Goal-Based Approach to Intelligent Information Retrieval"

Connected through hyperedge HE-3 (induced by source paper "A utility-based approach to learning in a mixed Case-Based and Model-Based Reasoning architecture"):
  with members:
    - "ADAPtER: an Integrated Diagnostic System Combining Case-Based and Abductive Reasoning"
    - "On the Usefulness of Re-using Diagnostic Solutions"
    - "A Performance Model for Knowledge-based Systems"

Connected through hyperedge HE-4 (induced by source paper "Design and Implementation of a Replay Framework based on a Partial Order Planner"):
  with members:
    - "Evaluating the Effectiveness of Derivation Replay in Partial-order vs State-space Planning"
    - "An Explanation-Based Approach to Improve Retrieval in Case-Based Planning"

Connected through hyperedge HE-5 (induced by source paper "AN EMPIRICAL APPROACH TO SOLVING THE GENERAL UTILITY PROBLEM IN SPEEDUP LEARNING"):
  with members:
    - "Unifying Empirical and Explanation-Based Learning by Modeling the Utility of Learned Knowledge"
    - "Simple Selection of Utile Control Rules in Speedup Learning"
    - "Using Genetic Algorithms for Supervised Concept 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