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: "``Learning Local Error Bars for Nonlinear Regression.''  Learning Local Error Bars for Nonlinear Regression"
  Abstract: We present a new method for obtaining local error bars for nonlinear regression, i.e., estimates of the confidence in predicted values that depend on the input. We approach this problem by applying a maximum-likelihood framework to an assumed distribution of errors. We demonstrate our method first on computer-generated data with locally varying, normally distributed target noise. We then apply it to laser data from the Santa Fe Time Series Competition where the underlying system noise is known quantization error and the error bars give local estimates of model misspecification. In both cases, the method also provides a weighted-regression effect that improves generalization performance.

Connected through hyperedge HE-1 (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"
    - "Evaluating Neural Network Predictors by Bootstrapping"

Connected through hyperedge HE-2 (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"
    - "Predictions with Confidence Intervals (Local Error Bars)"

Connected through hyperedge HE-3 (induced by source paper "NONLINEAR TRADING MODELS THROUGH SHARPE RATIO MAXIMIZATION"):
  with members:
    - "Nonlinear gated experts for time series: discovering regimes and avoiding overfitting"
    - "TO IMPROVE FORECASTING"

Connected through hyperedge HE-4 (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"
    - "Predictions with Confidence Intervals (Local Error Bars)"
    - "Predicting Conditional Probability Distributions: A Connectionist Approach"

Connected through hyperedge HE-5 (induced by source paper "Direct Multi-Step Time Series Prediction Using TD()"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS"

Connected through hyperedge HE-6 (induced by source paper "Predicting Conditional Probability Distributions: A Connectionist Approach"):
  with members:
    - "Avoiding overfitting by locally matching the noise level of the data gating network discovers the"
    - "The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING"

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