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: "Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions"
  Abstract: We describe and illustrate Bayesian approaches to modelling and analysis of multiple non-stationary time series. This begins with uni-variate models for collections of related time series assumedly driven by underlying but unobservable processes, referred to as dynamic latent factor processes. We focus on models in which the factor processes, and hence the observed time series, are modelled by time-varying autoregressions capable of flexibly representing ranges of observed non-stationary characteristics. We highlight concepts and new methods of time series decomposition to infer characteristics of latent components in time series, and relate uni-variate decomposition analyses to underlying multivariate dynamic factor structure. Our motivating application is in analysis of multiple EEG traces from an ongoing EEG study at Duke. In this study, individuals undergoing ECT therapy generate multiple EEG traces at various scalp locations, and physiological interest lies in identifying dependencies and dissimilarities across series. In addition to the multivariate and non-stationary aspects of the series, this area provides illustration of the new results about decomposition of time series into latent, physically interpretable components; this is illustrated in data analysis of one EEG data set. The paper also discusses current and future research directions. fl This research was supported in part by the National Science Foundation under grant DMS-9311071. The EEG data and context arose from discussions with Dr Andrew Krystal, of Duke University Medical Center, with whom continued interactions have been most valuable. Address for correspondence: Institute of Statistics and Decision Sciences, Duke University, Durham, NC 27708-0251 U.S.A. (http://www.stat.duke.edu)

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"
    - "BAYESIAN TIME SERIES: Models and Computations for the Analysis of Time Series in the Physical Sciences"
    - "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:
    - "BAYESIAN TIME SERIES: Models and Computations for the Analysis of Time Series in the Physical Sciences"

Connected through hyperedge HE-3 (induced by source paper "Priors and Component Structures in Autoregressive Time Series Models"):
  with members:
    - "Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models"
    - "Studies of Neurological Transmission Analysis using Hierarchical Bayesian Mixture Models"
    - "Signal Processing and Communications Reversible Jump Sampler for Autoregressive Time Series, Employing Full Conditionals to"
    - "Bayesian Inference on Periodicities and Component Spectral Structure in Time Series"

Connected through hyperedge HE-4 (induced by source paper "Bayesian Inference on Periodicities and Component Spectral Structure in Time Series"):
  with members:
    - "Priors and Component Structures in Autoregressive Time Series Models"

Connected through hyperedge HE-5 (induced by source paper "BAYESIAN TIME SERIES: Models and Computations for the Analysis of Time Series in the Physical Sciences"):
  with members:
    - "Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models"
    - "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