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: "Evaluating Neural Network Predictors by Bootstrapping"
  Abstract: We present a new method, inspired by the bootstrap, whose goal it is to determine the quality and reliability of a neural network predictor. Our method leads to more robust forecasting along with a large amount of statistical information on forecast performance that we exploit. We exhibit the method in the context of multi-variate time series prediction on financial data from the New York Stock Exchange. It turns out that the variation due to different resamplings (i.e., splits between training, cross-validation, and test sets) is significantly larger than the variation due to different network conditions (such as architecture and initial weights). Furthermore, this method allows us to forecast a probability distribution, as opposed to the traditional case of just a single value at each time step. We demonstrate this on a strictly held-out test set that includes the 1987 stock market crash. We also compare the performance of the class of neural networks to identically bootstrapped linear models.

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()"
    - "NONLINEAR TRADING MODELS THROUGH SHARPE RATIO MAXIMIZATION"
    - "Predictions with Confidence Intervals (Local Error Bars)"
    - "Predicting Conditional Probability Distributions: A Connectionist Approach"

Connected through hyperedge HE-2 (induced by source paper "The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING"):
  with members:
    - "Selecting Input Variables Using Mutual Information and Nonparametric Density Estimation"
    - "Predictions with Confidence Intervals (Local Error Bars)"
    - "Predicting Conditional Probability Distributions: A Connectionist Approach"

Connected through hyperedge HE-3 (induced by source paper "Predictions with Confidence Intervals (Local Error Bars)"):
  with members:
    - "Avoiding overfitting by locally matching the noise level of the data gating network discovers the"
    - "A comparison of some error estimates for neural network models  Summary"
    - "The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING"
    - "``Learning Local Error Bars for Nonlinear Regression.''  Learning Local Error Bars for Nonlinear Regression"

Connected through hyperedge HE-4 (induced by source paper "A comparison of some error estimates for neural network models  Summary"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "A Practical Bayesian Framework for Backprop Networks"
    - "Penalisation multiple adaptative un nouvel algorithme de regression, la penalisation multiple adapta-tive. Cet algorithme represente"
    - "Brief Papers Computing Second Derivatives in Feed-Forward Networks: A Review"
    - "Predictions with Confidence Intervals (Local Error Bars)"

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