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: "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.

Connected through hyperedge HE-1 (induced by source paper "On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance"):
  with members:
    - "Lessons in Neural Network Training: Overfitting Lessons in Neural Network Training: Overfitting May be Harder"
    - "Exponentially many local minima for single neurons"
    - "A Unifying View of Some Training Algorithms for Multilayer Perceptrons with FIR Filter Synapses"
    - "Statistical Evaluation of Neural Network Experiments: Minimum Requirements and Current Practice"

Connected through hyperedge HE-2 (induced by source paper "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"):
  with members:
    - "Neural Network Implementation in SAS R Software"
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Introduction to Radial Basis Function Networks"

Connected through hyperedge HE-3 (induced by source paper "Lessons in Neural Network Training: Overfitting Lessons in Neural Network Training: Overfitting May be Harder"):
  with members:
    - "Statistical Ideas for Selecting Network Architectures"
    - "On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance"
    - "Averaging and Data Snooping"

Connected through hyperedge HE-4 (induced by source paper "Statistical Ideas for Selecting Network Architectures"):
  with members:
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "Bayesian Graphical Models for Discrete Data"
    - "Lessons in Neural Network Training: Overfitting Lessons in Neural Network Training: Overfitting May be Harder"

Connected through hyperedge HE-5 (induced by source paper "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"):
  with members:
    - "Neural Networks with Quadratic VC Dimension"
    - "Sample Complexity for Learning Recurrent Perceptron Mappings"
    - "NEURAL NETS AS SYSTEMS MODELS AND CONTROLLERS suitability of "neural nets" as models for dynamical"
    - "On the Effect of Analog Noise in Discrete-Time Analog Computations"
    - "POWER OF NEURAL NETS"
    - "Networks of Spiking Neurons: The Third Generation of Neural Network Models"

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