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: "Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines"
  Abstract: A novel method for regression has been recently proposed by V. Vapnik et al. [8, 9]. The technique, called Support Vector Machine (SVM), is very well founded from the mathematical point of view and seems to provide a new insight in function approximation. We implemented the SVM and tested it on the same data base of chaotic time series that was used in [1] to compare the performances of different approximation techniques, including polynomial and rational approximation, local polynomial techniques, Radial Basis Functions, and Neural Networks. The SVM performs better than the approaches presented in [1]. We also study, for a particular time series, the variability in performance with respect to the few free parameters of SVM.

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 "Support Vector Machines: Training and Applications"):
  with members:
    - "Massive Data Discrimination via Linear Support Vector Machines"
    - "Human Face Detection in Visual Scenes"
    - "Extracting Support Data for a Given Task"

Connected through hyperedge HE-3 (induced by source paper "A Sparse Representation for Function Approximation"):
  with members:
    - "Learning networks for face analysis and synthesis"

Connected through hyperedge HE-4 (induced by source paper "PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS"):
  with members:
    - "A Delay Damage Model Selection Algorithm for NARX Neural Networks"
    - "A Practical Bayesian Framework for Backprop Networks"
    - "Neural net architectures for temporal sequence processing"
    - "Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference"
    - "Direct Multi-Step Time Series Prediction Using TD()"
    - "MIXED MEMORY MARKOV MODELS FOR TIME SERIES ANALYSIS"
    - "A comparison of neural net and conventional techniques for lighting control"
    - "A Nonparametric Bayesian Approach to Modelling Nonlinear Time Series"

Connected through hyperedge HE-5 (induced by source paper "Modeling Volatility using State Space Models"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "Nonlinear gated experts for time series: discovering regimes and avoiding overfitting"
    - "TO IMPROVE FORECASTING"
    - "A Bootstrap Evaluation of the Effect of Data Splitting on Financial Time Series"

Connected through hyperedge HE-6 (induced by source paper "Annealed Competition of Experts for a Segmentation and Classification of Switching Dynamics"):
  with members:
    - "Segmenting Time Series using Gated Experts with Simulated Annealing"
    - "Predicting Time Series with Support Vector Machines"
    - "Analysis of Drifting Dynamics with Neural Network Hidden Markov Models"

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