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: "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"
  Abstract: The primary goal of inductive learning is to generalize well that is, induce a function that accurately produces the correct output for future inputs. Hansen and Salamon showed that, under certain assumptions, combining the predictions of several separately trained neural networks will improve generalization. One of their key assumptions is that the individual networks should be independent in the errors they produce. In the standard way of performing backpropagation this assumption may be violated, because the standard procedure is to initialize network weights in the region of weight space near the origin. This means that backpropagation's gradient-descent search may only reach a small subset of the possible local minima. In this paper we present an approach to initializing neural networks that uses competitive learning to intelligently create networks that are originally located far from the origin of weight space, thereby potentially increasing the set of reachable local minima. We report experiments on two real-world datasets where combinations of networks initialized with our method generalize better than combina tions of networks initialized the traditional way.

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"
    - "Generating Accurate and Diverse Members of a Neural-Network Ensemble"

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"
    - "Generating Accurate and Diverse Members of a Neural-Network Ensemble"
    - "Experiments with a New Boosting Algorithm"

Connected through hyperedge HE-3 (induced by source paper "Actively Searching for an Effective Neural-Network Ensemble"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "An Empirical Evaluation of Bagging and Boosting"
    - "Learning from Bad Data"

Connected through hyperedge HE-4 (induced by source paper "Generating Accurate and Diverse Members of a Neural-Network Ensemble"):
  with members:
    - "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"
    - "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"
    - "An Empirical Evaluation of Bagging and Boosting"
    - "A New Metric-Based Approach to Model Selection"
    - "Using Neural Networks to Automatically Refine Expert System Knowledge Bases: Experiments in the NYNEX MAX Domain"

Connected through hyperedge HE-5 (induced by source paper "Pruning Recurrent Neural Networks for Improved Generalization Performance"):
  with members:
    - "A Delay Damage Model Selection Algorithm for NARX Neural Networks"
    - "Extraction of Rules from Discrete-Time Recurrent Neural Networks"
    - "Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks"
    - "Evolving Artificial Neural Networks using the Baldwin Effect"

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