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: "Space-Frequency Localized Basis Function Networks for Nonlinear System Estimation and Control"
  Abstract: Stable neural network control and estimation may be viewed formally as a merging of concepts from nonlinear dynamic systems theory with tools from multivariate approximation theory. This paper extends earlier results on adaptive control and estimation of nonlinear systems using gaussian radial basis functions to the on-line generation of irregularly sampled networks, using tools from multiresolution analysis and wavelet theory. This yields much more compact and efficient system representations while preserving global closed-loop stability. Approximation models employing basis functions that are localized in both space and spatial frequency admit a measure of the approximated function's spatial frequency content that is not directly dependent on reconstruction error. As a result, these models afford a means of adaptively selecting basis functions according to the local spatial frequency content of the approximated function. An algorithm for stable, on-line adaptation of output weights simultaneously with node configuration in a class of non-parametric models with wavelet basis functions is presented. An asymptotic bound on the error in the network's reconstruction is derived and shown to be dependent solely on the minimum approximation error associated with the steady state node configuration. In addition, prior bounds on the temporal bandwidth of the system to be identified or controlled are used to develop a criterion for on-line selection of radial and ridge wavelet basis functions, thus reducing the rate of increase in network's size with the dimension of the state vector. Experimental results obtained by using the network to predict the path of an unknown light bluff object thrown through air, in an active-vision based robotic catching system, are given to illustrate the network's performance in a simple real-time application.

Connected through hyperedge HE-1 (induced by source paper "Learning networks for face analysis and synthesis"):
  with members:
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"
    - "A Delay Damage Model Selection Algorithm for NARX Neural Networks"
    - "Equivalence of Linear Boltzmann Chains and Hidden Markov Models sequence L, is: where Z(; A;"
    - "Learning from incomplete data"
    - "PRIOR KNOWLEDGE AND THE CREATION OF "VIRTUAL" EXAMPLES FOR RBF NETWORKS 1"
    - "Regularization Theory and Neural Networks Architectures"
    - "In Advances in Neural Information Processing Systems 8  Gaussian Processes for Regression"
    - "Efficient Implementation of Gaussian Processes"

Connected through hyperedge HE-2 (induced by source paper "Minimax Estimation via Wavelet Shrinkage  a pleasure to acknowledge friendly conversations with Gerard Kerkyacharian,"):
  with members:
    - "Wavelet Shrinkage: Asymptopia?"
    - "Wavelet Thresholding via a Bayesian Approach"
    - "Nonlinear wavelet shrinkage with Bayes rules and Bayes factors 1"
    - "Density estimation by wavelet thresholding"
    - "Minimax Bayes, asymptotic minimax and sparse wavelet priors"
    - "Density and hazard rate estimation for right censored data using wavelet methods"
    - "Choice of Thresholds for Wavelet Shrinkage Estimate of the Spectrum fff j g are level-dependent"
    - "The Stationary Wavelet Transform and some Statistical Applications"

Connected through hyperedge HE-3 (induced by source paper "Adaptive Wavelet Control of Nonlinear Systems"):
  with members:
    - "An Analytical Framework for Local Feedforward Networks"

Connected through hyperedge HE-4 (induced by source paper "Priors, Stabilizers and Basis Functions: from regularization to radial, tensor and additive splines"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "Function Approximation with Neural Networks and Local Methods: Bias, Variance and Smoothness"

Connected through hyperedge HE-5 (induced by source paper "Identification and Control of Nonlinear Systems Using Neural Network Models: Design and Stability Analysis"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Learning networks for face analysis and synthesis"
    - "NEURAL NETS AS SYSTEMS MODELS AND CONTROLLERS suitability of "neural nets" as models for dynamical"
    - "FEEDBACK STABILIZATION USING TWO-HIDDEN-LAYER NETS"

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