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: "Segmenting Time Series using Gated Experts with Simulated Annealing"
  Abstract: Many real-world time series are multi-stationary, where the underlying data generating process (DGP) switches between different stationary subprocesses, or modes of operation. An important problem in modeling such systems is to discover the underlying switching process, which entails identifying the number of subprocesses and the dynamics of each subprocess. For many time series, this problem is ill-defined, since there are often no obvious means to distinguish the different subprocesses. We discuss the use of nonlinear gated experts to perform the segmentation and system identification of the time series. Unlike standard gated experts methods, however, we use concepts from statistical physics to enhance the segmentation for high-noise problems where only a few experts are required.

Connected through hyperedge HE-1 (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"
    - "Forecasting electricity demand using nonlinear mixture of experts"
    - "Introduction to Radial Basis Function Networks"
    - "Modeling Volatility using State Space Models"
    - "NONLINEAR TRADING MODELS THROUGH SHARPE RATIO MAXIMIZATION"

Connected through hyperedge HE-2 (induced by source paper "Annealed Competition of Experts for a Segmentation and Classification of Switching Dynamics"):
  with members:
    - "Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines"
    - "Predicting Time Series with Support Vector Machines"
    - "Analysis of Drifting Dynamics with Neural Network Hidden Markov Models"

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