human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "AN EMPIRICAL APPROACH TO SOLVING THE GENERAL UTILITY PROBLEM IN SPEEDUP LEARNING"):
  Abstract: The utility problem in speedup learning describes a common behavior of machine learning methods: the eventual degradation of performance due to increasing amounts of learned knowledge. The shape of the learning curve (cost of using a learning method vs. number of training examples) over several domains suggests a parameterized model relating performance to the amount of learned knowledge and a mechanism to limit the amount of learned knowledge for optimal performance. 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. Experimentation illustrates the existence of a minimum (representing least cost) in the learning curve which is reached after a few training examples. Stress is placed on controlling the amount of learned knowledge as opposed to which knowledge. An attempt is also made to relate domain characteristics to the shape of the learning curve.

Includes members:
  Member "Unifying Empirical and Explanation-Based Learning by Modeling the Utility of Learned Knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Simple Selection of Utile Control Rules in Speedup Learning")
  Member "Simple Selection of Utile Control Rules in Speedup Learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Unifying Empirical and Explanation-Based Learning by Modeling the Utility of Learned Knowledge")
  Member "Using Genetic Algorithms for Supervised Concept Learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.")
  Member "A Comparative Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "An Explanation-Based Approach to Improve Retrieval in Case-Based Planning")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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