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: "Priors and Component Structures in Autoregressive Time Series Models"
  Abstract: New approaches to prior specification and structuring in autoregressive time series models are introduced and developed. We focus on defining classes of prior distributions for parameters and latent variables related to latent components of an autoregressive model for an observed time series. These new priors naturally permit the incorporation of both qualitative and quantitative prior information about the number and relative importance of physically meaningful components that represent low frequency trends, quasi-periodic sub-processes, and high frequency residual noise components of observed series. The class of priors also naturally incorporates uncertainty about model order, and hence leads in posterior analysis to model order assessment and resulting posterior and predictive inferences that incorporate full uncertainties about model order as well as model parameters. Analysis also formally incorporates uncertainty, and leads to inferences about, unknown initial values of the time series, as it does for predictions of future values. Posterior analysis involves easily implemented iterative simulation methods, developed and described here. One motivating applied field is climatology, where the evaluation of latent structure, especially quasi-periodic structure, is of critical importance in connection with issues of global climatic variability. We explore analysis of data from the Southern Oscillation Index (SOI), one of several series that has been central in recent high-profile debates in the atmospheric sciences about recent apparent trends in climatic indicators.

Connected through hyperedge HE-1 (induced by source paper "Studies of Neurological Transmission Analysis using Hierarchical Bayesian Mixture Models"):
  with members:
    - "Mixture Models in the Exploration of Structure-Activity Relationships in Drug Design"
    - "Computing Nonparametric Hierarchical Models"
    - "Practical Bayesian Inference Using Mixtures of Mixtures"

Connected through hyperedge HE-2 (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"
    - "FILTERING VIA SIMULATION: AUXILIARY PARTICLE FILTERS"
    - "Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions"
    - "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-3 (induced by source paper "Signal Processing and Communications Reversible Jump Sampler for Autoregressive Time Series, Employing Full Conditionals to"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "Bayesian Inference on Periodicities and Component Spectral Structure in Time Series"):
  with members:
    - "Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions"

Connected through hyperedge HE-5 (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"
    - "BAYESIAN TIME SERIES: Models and Computations for the Analysis of Time Series in the Physical Sciences"
    - "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