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 "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.

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 "A comparison of some error estimates for neural network models  Summary":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Penalisation multiple adaptative un nouvel algorithme de regression, la penalisation multiple adapta-tive. Cet algorithme represente")
  Member "The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Selecting Input Variables Using Mutual Information and Nonparametric Density Estimation")
  Member "Evaluating Neural Network Predictors by Bootstrapping":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "``Learning Local Error Bars for Nonlinear Regression.''  Learning Local Error Bars for Nonlinear Regression")
  Member "``Learning Local Error Bars for Nonlinear Regression.''  Learning Local Error Bars for Nonlinear Regression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Evaluating Neural Network Predictors by Bootstrapping")

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