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: "Analysis of Drifting Dynamics with Neural Network Hidden Markov Models"
  Abstract: We present a method for the analysis of nonstationary time series with multiple operating modes. In particular, it is possible to detect and to model both a switching of the dynamics and a less abrupt, time consuming drift from one mode to another. This is achieved in two steps. First, an unsupervised training method provides prediction experts for the inherent dynamical modes. Then, the trained experts are used in a hidden Markov model that allows to model drifts. An application to physiological wake/sleep data demonstrates that analysis and modeling of real-world time series can be improved when the drift paradigm is taken into account.

Connected through hyperedge HE-1 (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"
    - "Segmenting Time Series using Gated Experts with Simulated Annealing"
    - "Predicting Time Series with Support Vector Machines"

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