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 "What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation"):
  Abstract: Technical Report UMIACS-TR-96-22 and CS-TR-3617 Institute for Advanced Computer Studies University of Maryland College Park, MD 20742 Abstract One of the most important aspects of any machine learning paradigm is how it scales according to problem size and complexity. Using a task with known optimal training error, and a pre-specified maximum number of training updates, we investigate the convergence of the backpropagation algorithm with respect to a) the complexity of the required function approximation, b) the size of the network in relation to the size required for an optimal solution, and c) the degree of noise in the training data. In general, for a) the solution found is worse when the function to be approximated is more complex, for b) oversized networks can result in lower training and generalization error in certain cases, and for c) the use of committee or ensemble techniques can be more beneficial as the level of noise in the training data is increased. For the experiments we performed, we do not obtain the optimal solution in any case. We further support the observation that larger networks can produce better training and generalization error using a face recognition example where a network with many more parameters than training points generalizes better than smaller networks.

Includes members:
  Member "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "POWER OF NEURAL NETS")
  Member "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Neural Network Implementation in SAS R Software")
  Member "Statistical Ideas for Selecting Network Architectures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Model Selection and Accounting for Model Uncertainty in Linear Regression Models")
  Member "Lessons in Neural Network Training: Overfitting Lessons in Neural Network Training: Overfitting May be Harder":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance")
  Member "On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Lessons in Neural Network Training: Overfitting Lessons in Neural Network Training: Overfitting May be Harder")

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