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 "Generating Accurate and Diverse Members of a Neural-Network Ensemble"):
  Abstract: Neural-network ensembles have been shown to be very accurate classification techniques. Previous work has shown that an effective ensemble should consist of networks that are not only highly correct, but ones that make their errors on different parts of the input space as well. Most existing techniques, however, only indirectly address the problem of creating such a set of networks. In this paper we present a technique called Addemup that uses genetic algorithms to directly search for an accurate and diverse set of trained networks. Addemup works by first creating an initial population, then uses genetic operators to continually create new networks, keeping the set of networks that are as accurate as possible while disagreeing with each other as much as possible. Experiments on three DNA problems show that Addemup is able to generate a set of trained networks that is more accurate than several existing approaches. Experiments also show that Addemup is able to effectively incorporate prior knowledge, if available, to improve the quality of its ensemble.

Includes members:
  Member "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms")
  Member "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs")
  Member "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms")
  Member "An Empirical Evaluation of Bagging and Boosting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks")
  Member "A New Metric-Based Approach to Model Selection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection")
  Member "Using Neural Networks to Automatically Refine Expert System Knowledge Bases: Experiments in the NYNEX MAX Domain":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Learning from Bad Data")

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: Neural_Networks