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: "BAYESIAN TIME SERIES: Models and Computations for the Analysis of Time Series in the Physical Sciences"
  Abstract: This articles discusses developments in Bayesian time series mod-elling and analysis relevant in studies of time series in the physical and engineering sciences. With illustrations and references, we discuss: Bayesian inference and computation in various state-space models, with examples in analysing quasi-periodic series; isolation and modelling of various components of error in time series; decompositions of time series into significant latent subseries; nonlinear time series models based on mixtures of auto-regressions; problems with errors and uncertainties in the timing of observations; and the development of non-linear models based on stochastic deformations of time scales.

Connected through hyperedge HE-1 (induced by source paper "Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models"):
  with members:
    - "BAYESIAN STATISTICS 6, pp. 000--000  Exact sampling for Bayesian inference: towards general purpose algorithms"
    - "Change Point and Change Curve Modeling in Stochastic Processes and Spatial Statistics"
    - "On Sequential Simulation-Based Methods for Bayesian Filtering"
    - "Analysis of hospital quality monitors using hierarchical time series models"
    - "Priors and Component Structures in Autoregressive Time Series Models"
    - "FILTERING VIA SIMULATION: AUXILIARY PARTICLE FILTERS"
    - "Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions"
    - "STUDIES OF QUALITY MONITOR TIME SERIES: THE V.A. HOSPITAL SYSTEM build on foundational contributions in"

Connected through hyperedge HE-2 (induced by source paper "Modelling and robustness issues in Bayesian time series analysis"):
  with members:
    - "Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions"

Connected through hyperedge HE-3 (induced by source paper "Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions"):
  with members:
    - "Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models"
    - "Priors and Component Structures in Autoregressive Time Series Models"
    - "Bayesian Inference on Periodicities and Component Spectral Structure in Time Series"
    - "Modelling and robustness issues in Bayesian time series analysis"

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