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: "A Delay Damage Model Selection Algorithm for NARX Neural Networks"
  Abstract: Recurrent neural networks have become popular models for system identification and time series prediction. NARX (Nonlinear AutoRegressive models with eXogenous inputs) neural network models are a popular subclass of recurrent networks and have been used in many applications. Though embedded memory can be found in all recurrent network models, it is particularly prominent in NARX models. We show that using intelligent memory order selection through pruning and good initial heuristics significantly improves the generalization and predictive performance of these nonlinear systems on problems as diverse as grammatical inference and time series prediction.

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"
    - "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"
    - "for Projective Basis Function Networks 2m1 Global Form 2m Local Form With appropriate constant factors,"

Connected through hyperedge HE-2 (induced by source paper "Neural Learning of Chaotic Dynamics: The Error Propagation Algorithm trains a neural network to identify"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS"):
  with members:
    - "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()"
    - "Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines"
    - "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-4 (induced by source paper "Pruning Recurrent Neural Networks for Improved Generalization Performance"):
  with members:
    - "Extraction of Rules from Discrete-Time Recurrent Neural Networks"
    - "Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks"
    - "Evolving Artificial Neural Networks using the Baldwin Effect"
    - "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"

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