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: "Simple Selection of Utile Control Rules in Speedup Learning"
  Abstract: Many recent approaches to avoiding the utility problem in speedup learning rely on sophisticated utility measures and significant numbers of training data to accurately estimate the utility of control knowledge. Empirical results presented here and elsewhere indicate that a simple selection strategy of retaining all control rules derived from a training problem explanation quickly defines an efficient set of control knowledge from few training problems. This simple selection strategy provides a low-cost alternative to example-intensive approaches for improving the speed of a problem solver.

Connected through hyperedge HE-1 (induced by source paper "Unifying Empirical and Explanation-Based Learning by Modeling the Utility of Learned Knowledge"):
  with members:
    - "Concept Learning and the Problem of Small"
    - "AN EMPIRICAL APPROACH TO SOLVING THE GENERAL UTILITY PROBLEM IN SPEEDUP LEARNING"

Connected through hyperedge HE-2 (induced by source paper "A Statistical Approach to Solving the EBL Utility Problem"):
  with members:
    - "A Formal Framework for Speedup Learning from Problems and Solutions"
    - "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation"
    - "Learning Default Concepts"
    - "Learning an Optimally Accurate Representational System"
    - "Probably Approximately Optimal Derivation Strategies"
    - "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment"

Connected through hyperedge HE-3 (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"
    - "Using Genetic Algorithms for Supervised Concept Learning"
    - "A Comparative Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems"

Connected through hyperedge HE-4 (induced by source paper "Utilization Filtering a method for reducing the inherent harmfulness of deductively learned knowledge field of"):
  with members:
    - "A Heuristic Approach to the Discovery of Macro-operators. Machine Learning, 3, 285-317. L e a"
    - "Learning Approximate Control Rules Of High Utility"

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