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 "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.

Includes members:
  Member "Pruning Recurrent Neural Networks for Improved Generalization Performance":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks")
  Member "Actively Searching for an Effective Neural-Network Ensemble":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.")
  Member "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms")
  Member "Generating Accurate and Diverse Members of a Neural-Network Ensemble":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "Actively Searching for an Effective Neural-Network Ensemble")

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