human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "``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.

Includes members:
  Member "Avoiding overfitting by locally matching the noise level of the data gating network discovers the":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Forecasting electricity demand using nonlinear mixture of experts")
  Member "Direct Multi-Step Time Series Prediction Using TD()":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,")
  Member "NONLINEAR TRADING MODELS THROUGH SHARPE RATIO MAXIMIZATION":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "TO IMPROVE FORECASTING")
  Member "Evaluating Neural Network Predictors by Bootstrapping":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING")
  Member "Predictions with Confidence Intervals (Local Error Bars)":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING")
  Member "Predicting Conditional Probability Distributions: A Connectionist Approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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