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: "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.

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"
    - "Simple Selection of Utile Control Rules in Speedup Learning"

Connected through hyperedge HE-2 (induced by source paper "A Comparative Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems"):
  with members:
    - "The Use of Explicit Goals for Knowledge to Guide Inference and 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-3 (induced by source paper "Using Genetic Algorithms for Supervised Concept Learning"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Using Real-Valued Genetic Algorithms to Evolve Rule Sets for Classification"
    - "Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems"
    - "Knowledge-Based Genetic Learning"
    - "Data Analyses Using Simulated Breeding and Inductive Learning Methods"
    - "Adaptive Strategy Selection for Concept Learning"
    - "A Survey of Evolution Strategies"
    - "Is Consistency Harmful?"

Connected through hyperedge HE-4 (induced by source paper "Simple Selection of Utile Control Rules in Speedup Learning"):
  with members:
    - "A Statistical Approach to Solving the EBL Utility Problem"
    - "Unifying Empirical and Explanation-Based Learning by Modeling the Utility of Learned Knowledge"
    - "Utilization Filtering a method for reducing the inherent harmfulness of deductively learned knowledge field of"

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