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: "Introduction to Radial Basis Function Networks"
  Abstract: This document is an introduction to radial basis function (RBF) networks, a type of artificial neural network for application to problems of supervised learning (e.g. regression, classification and time series prediction). It is available in either PostScript or hyper-text 2 .

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

Connected through hyperedge HE-2 (induced by source paper "Growing Cell Structures A Self-organizing Network for Unsupervised and Supervised Learning"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "GROWING RADIAL BASIS FUNCTION NETWORKS"
    - "Scatter-partitioning RBF network for function regression and image"
    - "Evolving Artificial Neural Networks using the Baldwin Effect"
    - "LBG-U method for vector quantization an improvement over LBG inspired from neural networks"
    - "Incremental Grid Growing: Encoding High-Dimensional Structure into a Two-Dimensional Feature Map"

Connected through hyperedge HE-3 (induced by source paper "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"):
  with members:
    - "Representing and Learning Visual Schemas in Neural Networks for Scene Analysis"
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Dyslexic and Category-Specific Aphasic Impairments in a Self-Organizing Feature Map Model of the Lexicon"
    - "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"
    - "Boltzmann Machine learning using mean field theory and linear response correction"
    - "Induction of Multiscale Temporal Structure"

Connected through hyperedge HE-4 (induced by source paper "Nonlinear gated experts for time series: discovering regimes and avoiding overfitting"):
  with members:
    - "First experiments using a mixture of nonlinear experts for time series prediction"
    - "Non-linear Models for Time Series Using Mixtures of Experts"
    - "Forecasting electricity demand using nonlinear mixture of experts"
    - "Modeling Volatility using State Space Models"
    - "Segmenting Time Series using Gated Experts with Simulated Annealing"
    - "NONLINEAR TRADING MODELS THROUGH SHARPE RATIO MAXIMIZATION"

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