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: "Statistical Ideas for Selecting Network Architectures"
  Abstract: Choosing the architecture of a neural network is one of the most important problems in making neural networks practically useful, but accounts of applications usually sweep these details under the carpet. How many hidden units are needed? Should weight decay be used, and if so how much? What type of output units should be chosen? And so on. We address these issues within the framework of statistical theory for model This paper is principally concerned with architecture selection issues for feed-forward neural networks (also known as multi-layer perceptrons). Many of the same issues arise in selecting radial basis function networks, recurrent networks and more widely. These problems occur in a much wider context within statistics, and applied statisticians have been selecting and combining models for decades. Two recent discussions are [4, 5]. References [3, 20, 21, 22] discuss neural networks from a statistical perspective. choice, which provides a number of workable approximate answers.

Connected through hyperedge HE-1 (induced by source paper "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"):
  with members:
    - "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models"
    - "The out-of-bootstrap method for model averaging and selection"
    - "Stacked Density Estimation"
    - "Bayesian Graphical Models for Discrete Data"
    - "Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance"
    - "Model Selection for Generalized Linear Models via GLIB, with Application to Epidemiology 1"
    - "Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models"
    - "Parallel Markov chain Monte Carlo sampling."

Connected through hyperedge HE-2 (induced by source paper "Lessons in Neural Network Training: Overfitting Lessons in Neural Network Training: Overfitting May be Harder"):
  with members:
    - "What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation"
    - "On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance"
    - "Averaging and Data Snooping"

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,"
    - "Lessons in Neural Network Training: Overfitting Lessons in Neural Network Training: Overfitting May be Harder"
    - "On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance"

Connected through hyperedge HE-4 (induced by source paper "Bayesian Graphical Models for Discrete Data"):
  with members:
    - "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models"
    - ""Linear Dependencies Represented by Chain Graphs," "Graphical Modelling With MIM," Manual. "Identifying Independence in Bayesian"
    - "Model Selection and Accounting for Model Uncertainty in Linear Regression Models"
    - "Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance"
    - "Model Selection for Generalized Linear Models via GLIB, with Application to Epidemiology 1"
    - "Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models"
    - "Decomposable graphical Gaussian model determination"
    - "Bayesian Graphical Modeling for Intelligent Tutoring Systems"

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