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: "Avoiding overfitting by locally matching the noise level of the data gating network discovers the"
  Abstract: When trying to forecast the future behavior of a real-world system, two of the key problems are nonstationarity of the process (e.g., regime switching) and overfitting of the model (particularly serious for noisy processes). This articles shows how gated experts can point to solutions to these problems. The architecture, also called society of experts and mixture of experts consists of a (nonlinear) gating network and several (nonlinear) competing experts. Each expert learns a conditional mean (as usual), but each expert also has its own adaptive width. The gating network learns to assign a probability to each expert that depends on the input. This article first discusses the assumptions underlying this architecture and derives the weight update rules. It then evaluates the performance of gated experts in comparison to that of single networks, as well as to networks with two outputs, one predicting the mean, the other one the local error bar. This article also investigates the ability of gated experts to discover and characterize underlying the regimes. The results are: * there is significantly less overfitting compared to single nets, for two reasons: only subsets of the potential inputs are given to the experts and gating network (less of a curse of dimensionality), and the experts learn to match their variances to the (local) noise levels, thus only learning as This article focuses on the architecture and the overfitting problem. Applications to a computer-generated toy problem and the laser data from Santa Fe Competition are given in [Mangeas and Weigend, 1995], and the application to the real-world problem of predicting the electricity demand of France are given in [Mangeas et al., 1995]. much as the data support.

Connected through hyperedge HE-1 (induced by source paper "Forecasting electricity demand using nonlinear mixture of experts"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Nonlinear gated experts for time series: discovering regimes and avoiding overfitting"

Connected through hyperedge HE-2 (induced by source paper "``Learning Local Error Bars for Nonlinear Regression.''  Learning Local Error Bars for Nonlinear Regression"):
  with members:
    - "Direct Multi-Step Time Series Prediction Using TD()"
    - "NONLINEAR TRADING MODELS THROUGH SHARPE RATIO MAXIMIZATION"
    - "Evaluating Neural Network Predictors by Bootstrapping"
    - "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:
    - "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"
    - "``Learning Local Error Bars for Nonlinear Regression.''  Learning Local Error Bars for Nonlinear Regression"

Connected through hyperedge HE-4 (induced by source paper "Hierarchical Mixtures of Experts and the EM Algorithm"):
  with members:
    - "Validation of Average Error Rate Over Classifiers"
    - "A HIERARCHICAL COMMUNITY OF EXPERTS"
    - "Comparison of Neural and Statistical Classifiers| Theory and Practice"
    - "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"
    - "Supervised learning from incomplete data via an EM approach"
    - "A Statistical Approach to Decision Tree Modeling"
    - "On learning hierarchical classifications"
    - "Using Neural Networks for Descriptive Statistical Analysis of Educational Data"

Connected through hyperedge HE-5 (induced by source paper "Predicting Conditional Probability Distributions: A Connectionist Approach"):
  with members:
    - "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"

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