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: "Lessons in Neural Network Training: Overfitting Lessons in Neural Network Training: Overfitting May be Harder"
  Abstract: For many reasons, neural networks have become very popular AI machine learning models. Two of the most important aspects of machine learning models are how well the model generalizes to unseen data, and how well the model scales with problem complexity. Using a controlled task with known optimal training error, we investigate the convergence of the backpropagation (BP) algorithm. We find that the optimal solution is typically not found. Furthermore, we observe that networks larger than might be expected can result in lower training and generalization error. This result is supported by another real world example. We further investigate the training behavior by analyzing the weights in trained networks (excess degrees of freedom are seen to do little harm and to aid convergence), and contrasting the interpolation characteristics of multi-layer perceptron neural networks (MLPs) and polynomial models (overfitting behavior is very different the MLP is often biased towards smoother solutions). Finally, we analyze relevant theory outlining the reasons for significant practical differences. These results bring into question common beliefs about neural network training regarding convergence and optimal network size, suggest alternate guidelines for practical use (lower fear of excess degrees of freedom), and help to direct future work (e.g. methods for creation of more parsimonious solutions, importance of the MLP/BP bias and possibly worse performance of improved training algorithms).

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:
    - "What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation"
    - "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 "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"
    - "What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation"

Connected through hyperedge HE-3 (induced by source paper "What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation"):
  with members:
    - "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"
    - "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"
    - "Statistical Ideas for Selecting Network Architectures"
    - "On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance"

Connected through hyperedge HE-4 (induced by source paper "Averaging and Data Snooping"):
  with members:
    - "A Quantitative Study of Experimental Evaluations of Neural Network Learning Algorithms: Current Research Practice"
    - "Statistical Evaluation of Neural Network Experiments: Minimum Requirements and Current Practice"

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