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: "On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance"
  Abstract: Andrew D. Back was with the Department of Electrical and Computer Engineering, University of Queensland. St. Lucia, Australia. He is now with the Brain Information Processing Group, Frontier Research Program, RIKEN, The Institute of Physical and Chemical Research, 2-1 Hirosawa, Wako-shi, Saitama 351-01, Japan Abstract The performance of neural network simulations is often reported in terms of the mean and standard deviation of a number of simulations performed with different starting conditions. However, in many cases, the distribution of the individual results does not approximate a Gaussian distribution, may not be symmetric, and may be multimodal. We present the distribution of results for practical problems and show that assuming Gaussian distributions can significantly affect the interpretation of results, especially those of comparison studies. For a controlled task which we consider, we find that the distribution of performance is skewed towards better performance for smoother target functions and skewed towards worse performance

Connected through hyperedge HE-1 (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"
    - "What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation"
    - "Averaging and Data Snooping"

Connected through hyperedge HE-2 (induced by source paper "Exponentially many local minima for single neurons"):
  with members:
    - "BACKPROPAGATION CAN GIVE RISE TO SPURIOUS LOCAL MINIMA EVEN FOR NETWORKS WITHOUT HIDDEN LAYERS"
    - "BACKPROPAGATION SEPARATES WHERE PERCEPTRONS DO"
    - "Relative Loss Bounds for Multidimensional Regression Problems"

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"
    - "Lessons in Neural Network Training: Overfitting Lessons in Neural Network Training: Overfitting May be Harder"

Connected through hyperedge HE-4 (induced by source paper "A Unifying View of Some Training Algorithms for Multilayer Perceptrons with FIR Filter Synapses"):
  with members:
    (none)

Connected through hyperedge HE-5 (induced by source paper "Statistical Evaluation of Neural Network Experiments: Minimum Requirements and Current Practice"):
  with members:
    - "A Quantitative Study of Experimental Evaluations of Neural Network Learning Algorithms: Current Research Practice"
    - "Averaging and Data Snooping"

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