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: "Predictions with Confidence Intervals (Local Error Bars)"
  Abstract: We present a new method for obtaining local error bars, i.e., estimates of the confidence in the predicted value that depend on the input. We approach this problem of nonlinear regression in a maximum likelihood framework. We demonstrate our technique first on computer generated data with locally varying, normally distributed target noise. We then apply it to the laser data from the Santa Fe Time Series Competition. Finally, we extend the technique to estimate error bars for iterated predictions, and apply it to the exact competition task where it gives the best performance to date.

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"
    - "Evaluating Neural Network Predictors by Bootstrapping"
    - "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"
    - "Evaluating Neural Network Predictors by Bootstrapping"
    - "Predicting Conditional Probability Distributions: A Connectionist Approach"

Connected through hyperedge HE-3 (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"
    - "Evaluating Neural Network Predictors by Bootstrapping"

Connected through hyperedge HE-4 (induced by source paper "Evaluating Neural Network Predictors by Bootstrapping"):
  with members:
    - "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-5 (induced by source paper "Avoiding overfitting by locally matching the noise level of the data gating network discovers the"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Forecasting electricity demand using nonlinear mixture of experts"
    - "``Learning Local Error Bars for Nonlinear Regression.''  Learning Local Error Bars for Nonlinear Regression"
    - "Predicting Conditional Probability Distributions: A Connectionist Approach"

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