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: "Forecasting electricity demand using nonlinear mixture of experts"
  Abstract: In this paper we study a forecasting model based on mixture of experts for predicting the French electric daily consumption energy. We split the task into two parts. Using mixture of experts, a first model predicts the electricity demand from the exogenous variables (such as temperature and degree of cloud cover) and can be viewed as a nonlinear regression model of mixture of Gaussians. Using a single neural network, a second model predicts the evolution of the residual error of the first one, and can be viewed as an nonlinear autoregression model. We analyze the splitting of the input space generated by the mixture of experts model, and compare the performance to models presently used.

Connected through hyperedge HE-1 (induced by source paper "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."):
  with members:
    - "Multiassociative Memory"
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Self-Organization and Functional Role of Lateral Connections and Multisize Receptive Fields in the Primary Visual Cortex"
    - "Neural Network Applicability: Classifying the Problem Space"
    - "Competitive Anti-Hebbian Learning of Invariants"
    - "Multiple Network Systems (Minos) Modules: Task Division and Module Discrimination 1"
    - "Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques"

Connected through hyperedge HE-2 (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-3 (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"
    - "Predictions with Confidence Intervals (Local Error Bars)"
    - "``Learning Local Error Bars for Nonlinear Regression.''  Learning Local Error Bars for Nonlinear Regression"
    - "Predicting Conditional Probability Distributions: A Connectionist Approach"

Connected through hyperedge HE-4 (induced by source paper "Nonlinear gated experts for time series: discovering regimes and avoiding overfitting"):
  with members:
    - "First experiments using a mixture of nonlinear experts for time series prediction"
    - "Non-linear Models for Time Series Using Mixtures of Experts"
    - "Introduction to Radial Basis Function Networks"
    - "Modeling Volatility using State Space Models"
    - "Segmenting Time Series using Gated Experts with Simulated Annealing"
    - "NONLINEAR TRADING MODELS THROUGH SHARPE RATIO MAXIMIZATION"

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