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 "Modelling and robustness issues in Bayesian time series analysis"):
  Abstract: Some areas of recent development and current interest in time series are noted, with some discussion of Bayesian modelling efforts motivated by substantial practical problems. The areas include non-linear auto-regressive time series modelling, measurement error structures in state-space modelling of time series, and issues of timing uncertainties and time deformations. Some discussion of the needs and opportunities for work on non/semi-parametric models and robustness issues is given in each context.

Includes members:
  Member "Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models")
  Member "BAYESIAN TIME SERIES: Models and Computations for the Analysis of Time Series in the Physical Sciences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models")

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