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: "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.

Connected through hyperedge HE-1 (induced by source paper "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"):
  with members:
    - "Error Reduction through Learning Multiple Descriptions"
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "An Empirical Evaluation of Bagging and Boosting"):
  with members:
    - "Improving Bagging Performance by Increasing Decision Tree Diversity"
    - "Actively Searching for an Effective Neural-Network Ensemble"
    - "Experiments with a New Boosting Algorithm"
    - "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"

Connected through hyperedge HE-3 (induced by source paper "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"):
  with members:
    - "Pruning Recurrent Neural Networks for Improved Generalization Performance"
    - "Actively Searching for an Effective Neural-Network Ensemble"
    - "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"
    - "An Empirical Evaluation of Bagging and Boosting"

Connected through hyperedge HE-4 (induced by source paper "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"):
  with members:
    - "A System for Induction of Oblique Decision Trees"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs"

Connected through hyperedge HE-5 (induced by source paper "Using Neural Networks to Automatically Refine Expert System Knowledge Bases: Experiments in the NYNEX MAX Domain"):
  with members:
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"
    - "Extracting Tree-Structured Representations of Trained Networks"
    - "Learning from Bad Data"

Connected through hyperedge HE-6 (induced by source paper "A New Metric-Based Approach to Model Selection"):
  with members:
    - "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"
    - "Characterizing the generalization performance of model selection strategies"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"

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: Neural_Networks