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 Bootstrap Evaluation of the Effect of Data Splitting on Financial Time Series"
  Abstract: This article exposes problems of the commonly used technique of splitting the available data into training, validation, and test sets that are held fixed, warns about drawing too strong conclusions from such static splits, and shows potential pitfalls of ignoring variability across splits. Using a bootstrap or resampling method, we compare the uncertainty in the solution stemming from the data splitting with neural network specific uncertainties (parameter initialization, choice of number of hidden units, etc.). We present two results on data from the New York Stock Exchange. First, the variation due to different resamplings is significantly larger than the variation due to different network conditions. This result implies that it is important to not over-interpret a model (or an ensemble of models) estimated on one specific split of the data. Second, on each split, the neural network solution with early stopping is very close to a linear model; no significant nonlinearities are extracted.

Connected through hyperedge HE-1 (induced by source paper "TO IMPROVE FORECASTING"):
  with members:
    - "Modeling Volatility using State Space Models"
    - "NONLINEAR TRADING MODELS THROUGH SHARPE RATIO MAXIMIZATION"

Connected through hyperedge HE-2 (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"
    - "TO IMPROVE FORECASTING"

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