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 "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).

Includes members:
  Member "Statistical Ideas for Selecting Network Architectures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Model Selection and Accounting for Model Uncertainty in Linear Regression Models")
  Member "What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "Exponentially many local minima for single neurons")
  Member "Averaging and Data Snooping":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A Quantitative Study of Experimental Evaluations of Neural Network Learning Algorithms: Current Research Practice")

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