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 TRADING MODELS THROUGH SHARPE RATIO MAXIMIZATION"
  Abstract: Working Paper IS-97-005, Leonard N. Stern School of Business, New York University. In: Decision Technologies for Financial Engineering (Proceedings of the Fourth International Conference on Neural Networks in the Capital Markets, NNCM-96), pp. 3-22. Edited by A.S.Weigend, Y.S.Abu-Mostafa, and A.-P.N.Refenes. Singapore: World Scientific, 1997. http://www.stern.nyu.edu/~aweigend/Research/Papers/SharpeRatio While many trading strategies are based on price prediction, traders in financial markets are typically interested in risk-adjusted performance such as the Sharpe Ratio, rather than price predictions themselves. This paper introduces an approach which generates a nonlinear strategy that explicitly maximizes the Sharpe Ratio. It is expressed as a neural network model whose output is the position size between a risky and a risk-free asset. The iterative parameter update rules are derived and compared to alternative approaches. The resulting trading strategy is evaluated and analyzed on both computer-generated data and real world data (DAX, the daily German equity index). Trading based on Sharpe Ratio maximization compares favorably to both profit optimization and probability matching (through cross-entropy optimization). The results show that the goal of optimizing out-of-sample risk-adjusted profit can be achieved with this nonlinear approach.

Connected through hyperedge HE-1 (induced by source paper "``Learning Local Error Bars for Nonlinear Regression.''  Learning Local Error Bars for Nonlinear Regression"):
  with members:
    - "Avoiding overfitting by locally matching the noise level of the data gating network discovers the"
    - "Direct Multi-Step Time Series Prediction Using TD()"
    - "Evaluating Neural Network Predictors by Bootstrapping"
    - "Predictions with Confidence Intervals (Local Error Bars)"
    - "Predicting Conditional Probability Distributions: A Connectionist Approach"

Connected through hyperedge HE-2 (induced by source paper "TO IMPROVE FORECASTING"):
  with members:
    - "Modeling Volatility using State Space Models"
    - "A Bootstrap Evaluation of the Effect of Data Splitting on Financial Time Series"

Connected through hyperedge HE-3 (induced by source paper "Nonlinear gated experts for time series: discovering regimes and avoiding overfitting"):
  with members:
    - "First experiments using a mixture of nonlinear experts for time series prediction"
    - "Non-linear Models for Time Series Using Mixtures of Experts"
    - "Forecasting electricity demand using nonlinear mixture of experts"
    - "Introduction to Radial Basis Function Networks"
    - "Modeling Volatility using State Space Models"
    - "Segmenting Time Series using Gated Experts with Simulated Annealing"

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