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: "TO IMPROVE FORECASTING"
  Abstract: Working Paper IS-97-007, Leonard N. Stern School of Business, New York University. In: Journal of Computational Intelligence in Finance 6 (1998) 14-23. (Special Issue on "Improving Generalization of Nonlinear Financial Forecasting Models".) http://www.stern.nyu.edu/~aweigend/Research/Papers/InteractionLayer Abstract. Predictive models for financial data are often based on a large number of plausible inputs that are potentially nonlinearly combined to yield the conditional expectation of a target, such as a daily return of an asset. This paper introduces a new architecture for this task: On the output side, we predict dynamical variables such as first derivatives and curvatures on different time spans. These are subsequently combined in an interaction output layer to form several estimates of the variable of interest. Those estimates are then averaged to yield the final prediction. Independently from this idea, on the input side, we propose a new internal preprocessing layer connected with a diagonal matrix of positive weights to a layer of squashing functions. These weights adapt for each input individually and learn to squash outliers in the input. We apply these two ideas to the real world example of the daily predictions of the German stock index DAX (Deutscher Aktien Index), and compare the results to a network with a single output. The new six layer architecture is more stable in training due to two facts: (1) More information is flowing back from the outputs to the input in the backward pass; (2) The constraint of predicting first and second derivatives focuses the learning on the relevant variables for the dynamics. The architectures are compared from both the training perspective (squared errors, robust errors), and from the trading perspective (annualized returns, percent correct, Sharpe ratio).

Connected through hyperedge HE-1 (induced by source paper "A Bootstrap Evaluation of the Effect of Data Splitting on Financial Time Series"):
  with members:
    - "Modeling Volatility using State Space Models"

Connected through hyperedge HE-2 (induced by source paper "NONLINEAR TRADING MODELS THROUGH SHARPE RATIO MAXIMIZATION"):
  with members:
    - "Nonlinear gated experts for time series: discovering regimes and avoiding overfitting"
    - "``Learning Local Error Bars for Nonlinear Regression.''  Learning Local Error Bars for Nonlinear Regression"

Connected through hyperedge HE-3 (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"
    - "Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines"
    - "A Bootstrap Evaluation of the Effect of Data Splitting on Financial 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