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: "PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS"
  Abstract: We investigate the effectiveness of connectionist networks for predicting the future continuation of temporal sequences. The problem of overfitting, particularly serious for short records of noisy data, is addressed by the method of weight-elimination: a term penalizing network complexity is added to the usual cost function in back-propagation. The ultimate goal is prediction accuracy. We analyze two time series. On the benchmark sunspot series, the networks outperform traditional statistical approaches. We show that the network performance does not deteriorate when there are more input units than needed. Weight-elimination also manages to extract some part of the dynamics of the notoriously noisy currency exchange rates and makes the network solution interpretable.

Connected through hyperedge HE-1 (induced by source paper "MIXED MEMORY MARKOV MODELS FOR TIME SERIES ANALYSIS"):
  with members:
    - "Factorial Hidden Markov Models"

Connected through hyperedge HE-2 (induced by source paper "A Practical Bayesian Framework for Backprop Networks"):
  with members:
    - "Density Networks and their Application to Protein Modelling"
    - "FLAT MINIMA Neural Computation 9(1):1-42 (1997)"
    - "Using Neural Networks for Descriptive Statistical Analysis of Educational Data"
    - "Ensemble Learning and Evidence Maximization"
    - "Probabilistic Networks: New Models and New Methods"
    - "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"
    - "Bayesian Non-linear Modelling for the Prediction Competition"
    - "MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III)"

Connected through hyperedge HE-3 (induced by source paper "A Nonparametric Bayesian Approach to Modelling Nonlinear Time Series"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference"):
  with members:
    - "Draft Symbolic Representation of Neural Networks"
    - "Extraction of Rules from Discrete-Time Recurrent Neural Networks"
    - "POWER OF NEURAL NETS"
    - "Statistical Mechanics of Nonlinear Nonequilibrium Financial Markets: Applications to Optimized Trading"

Connected through hyperedge HE-5 (induced by source paper "Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "A Sparse Representation for Function Approximation"
    - "Modeling Volatility using State Space Models"
    - "Support Vector Machines: Training and Applications"
    - "Annealed Competition of Experts for a Segmentation and Classification of Switching Dynamics"

Connected through hyperedge HE-6 (induced by source paper "A Delay Damage Model Selection Algorithm for NARX Neural Networks"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "Pruning Recurrent Neural Networks for Improved Generalization Performance"
    - "Neural Learning of Chaotic Dynamics: The Error Propagation Algorithm trains a neural network to identify"

Connected through hyperedge HE-7 (induced by source paper "Neural net architectures for temporal sequence processing"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Induction of Multiscale Temporal Structure"
    - "A FIXED SIZE STORAGE O(n 3 TIME COMPLEXITY LEARNING ALGORITHM FOR FULLY RECURRENT CONTINUALLY RUNNING"
    - "RESONANCE AND THE PERCEPTION OF MUSICAL METER"

Connected through hyperedge HE-8 (induced by source paper "A comparison of neural net and conventional techniques for lighting control"):
  with members:
    - "The Neural Network House: An Overview"

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