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: "Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference"
  Abstract: Financial forecasting is an example of a signal processing problem which is challenging due to small sample sizes, high noise, non-stationarity, and non-linearity. Neural networks have been very successful in a number of signal processing applications. We discuss fundamental limitations and inherent difficulties when using neural networks for the processing of high noise, small sample size signals. We introduce a new intelligent signal processing method which addresses the difficulties. The method uses conversion into a symbolic representation with a self-organizing map, and grammatical inference with recurrent neural networks. We apply the method to the prediction of daily foreign exchange rates, addressing difficulties with non-stationarity, overfitting, and unequal a priori class probabilities, and we find significant predictability in comprehensive experiments covering 5 different foreign exchange rates. The method correctly predicts the direction of change for the next day with an error rate of 47.1%. The error rate reduces to around 40% when rejecting examples where the system has low confidence in its prediction. The symbolic representation aids the extraction of symbolic knowledge from the recurrent neural networks in the form of deterministic finite state automata. These automata explain the operation of the system and are often relatively simple. Rules related to well known behavior such as trend following and mean reversal are extracted.

Connected through hyperedge HE-1 (induced by source paper "POWER OF NEURAL NETS"):
  with members:
    - "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"
    - "Dimension of Recurrent Neural Networks"
    - "Facing The Facts: Necessary Requirements For The Artificial Evolution of Complex Behaviour"
    - "Fault-Tolerant Implementation of Finite-State Automata in Recurrent Neural Networks"
    - "Can Recurrent Neural Networks Learn Natural Language Grammars? W&Z recurrent neural networks are able to"
    - "Interconnected Automata and Linear Systems: A Theoretical Framework in Discrete-Time In Hybrid Systems III: Verification"
    - "First-Order vs. Second-Order Single Layer Recurrent Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "Extraction of Rules from Discrete-Time Recurrent Neural Networks"):
  with members:
    - "Pruning Recurrent Neural Networks for Improved Generalization Performance"
    - "Rule Revision with Recurrent Neural Networks"
    - "Analysis of Dynamical Recognizers"

Connected through hyperedge HE-3 (induced by source paper "Draft Symbolic Representation of Neural Networks"):
  with members:
    - "Evaluation and Ordering of Rules Extracted from Feedforward Networks"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"

Connected through hyperedge HE-4 (induced by source paper "Statistical Mechanics of Nonlinear Nonequilibrium Financial Markets: Applications to Optimized Trading"):
  with members:
    - "Volatility of Volatility of Financial Markets"
    - "%A L. Ingber %T Adaptive simulated annealing (ASA): Lessons learned %J Control and Cybernetics Annealing"
    - "STATISTICAL MECHANICS OF COMBAT WITH HUMAN FACTORS"
    - "Path-integral evolution of chaos embedded in noise: Duffing neocortical analog"
    - "Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG"
    - "NONLINEAR NONEQUILIBRIUM NONQUANTUM NONCHAOTIC STATISTICAL MECHANICS OF NEOCORTICAL INTERACTIONS"

Connected through hyperedge HE-5 (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"
    - "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"

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