human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "BAYESIAN STATISTICS 6, pp. 000--000  Exact sampling for Bayesian inference: towards general purpose algorithms")
  Member "Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models")
  Member "Studies of Neurological Transmission Analysis using Hierarchical Bayesian Mixture Models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Mixture Models in the Exploration of Structure-Activity Relationships in Drug Design")
  Member "Signal Processing and Communications Reversible Jump Sampler for Autoregressive Time Series, Employing Full Conditionals to":
    also connected through other hyperedges:
      (none)
  Member "Bayesian Inference on Periodicities and Component Spectral Structure in Time Series":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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