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: "Unifying Empirical and Explanation-Based Learning by Modeling the Utility of Learned Knowledge"
  Abstract: The overfit problem in empirical learning and the utility problem in explanation-based learning describe a similar phenomenon: the degradation of performance due to an increase in the amount of learned knowledge. Plotting the performance of learned knowledge during the course of learning (the performance response) reveals a common trend for several learning methods. Modeling this trend allows a control system to constrain the amount of learned knowledge to achieve peak performance and avoid the general utility problem. Experiments evaluate a particular empirical model of the trend, and analysis of the learners derive several formal models. If, as evidence suggests, the general utility problem can be modeled using the same mechanisms for different learning paradigms, then the model serves to unify the paradigms into one framework capable of comparing and selecting different learning methods based on predicted achievable performance.

Connected through hyperedge HE-1 (induced by source paper "AN EMPIRICAL APPROACH TO SOLVING THE GENERAL UTILITY PROBLEM IN SPEEDUP LEARNING"):
  with members:
    - "Simple Selection of Utile Control Rules in Speedup Learning"
    - "Using Genetic Algorithms for Supervised Concept Learning"
    - "A Comparative Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems"

Connected through hyperedge HE-2 (induced by source paper "Simple Selection of Utile Control Rules in Speedup Learning"):
  with members:
    - "A Statistical Approach to Solving the EBL Utility Problem"
    - "AN EMPIRICAL APPROACH TO SOLVING THE GENERAL UTILITY PROBLEM IN SPEEDUP LEARNING"
    - "Utilization Filtering a method for reducing the inherent harmfulness of deductively learned knowledge field of"

Connected through hyperedge HE-3 (induced by source paper "Concept Learning and the Problem of Small"):
  with members:
    - "Fossil: A Robust Relational Learner"
    - "The RISE System: Conquering Without Separating"
    - "Learning with Rare Cases and Small Disjuncts"
    - "The Problem with Noise and Small Disjuncts"
    - "Pessimistic and Optimistic Induction"
    - "On Learning Multiple Descriptions of a Concept"
    - "Using Partitioning to Speed Up Specific-to-General Rule Induction"

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