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: "A Nonparametric Bayesian Approach to Modelling Nonlinear Time Series"
  Abstract: The Bayesian multivariate adaptive regression spline (BMARS) methodology of Denison et al. (1997) is extended to cope with nonlinear time series and financial datasets. The nonlinear time series model is closely related to the adaptive spline threshold autoregressive (ASTAR) method of Lewis and Stevens (1991) while the financial models can be thought of as Bayesian versions of both the generalised and simple autoregressive conditional het-eroscadastic (GARCH and ARCH) models.

Connected through hyperedge HE-1 (induced by source paper "PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS"):
  with members:
    - "A Delay Damage Model Selection Algorithm for NARX Neural Networks"
    - "A Practical Bayesian Framework for Backprop Networks"
    - "Neural net architectures for temporal sequence processing"
    - "Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference"
    - "Direct Multi-Step Time Series Prediction Using TD()"
    - "Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines"
    - "MIXED MEMORY MARKOV MODELS FOR TIME SERIES ANALYSIS"
    - "A comparison of neural net and conventional techniques for lighting control"

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: Probabilistic_Methods