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 Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models"
  Abstract: Claudia Cargnoni is with the Dipartimento Statistico, Universita di Firenze, 50100 Firenze, Italy. Peter Muller is Assistant Professor, and Mike West is Professor, in the Institute of Statistics and Decision Sciences at Duke University, Durham NC 27708-0251. Research of Cargnoni was performed while visiting ISDS during 1995. Muller and West were partially supported by NSF under grant DMS-9305699.

Connected through hyperedge HE-1 (induced by source paper "BAYESIAN STATISTICS 6, pp. 000--000  Exact sampling for Bayesian inference: towards general purpose algorithms"):
  with members:
    - "Applications and extensions of MCMC in IRT: Multiple item types, missing data, and rated responses"
    - "On Bayesian analysis of mixtures with an unknown number of components  Summary"
    - "An Approach to Diagnosing Total Variation Convergence of MCMC Algorithms"

Connected through hyperedge HE-2 (induced by source paper "Change Point and Change Curve Modeling in Stochastic Processes and Spatial Statistics"):
  with members:
    - "Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models"
    - "A Note on the Dirichlet Process Prior in Bayesian Nonparametric Inference with Partial Exchangeability 1"

Connected through hyperedge HE-3 (induced by source paper "STUDIES OF QUALITY MONITOR TIME SERIES: THE V.A. HOSPITAL SYSTEM build on foundational contributions in"):
  with members:
    - "Analysis of hospital quality monitors using hierarchical time series models"

Connected through hyperedge HE-4 (induced by source paper "FILTERING VIA SIMULATION: AUXILIARY PARTICLE FILTERS"):
  with members:
    - "On Sequential Simulation-Based Methods for Bayesian Filtering"

Connected through hyperedge HE-5 (induced by source paper "Modelling Risk from a Disease in Time and Space"):
  with members:
    - "Hierarchical Spatio-Temporal Mapping of Disease Rates"
    - "Bayesian Analysis of Agricultural Field Experiments"

Connected through hyperedge HE-6 (induced by source paper "Priors and Component Structures in Autoregressive Time Series Models"):
  with members:
    - "Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions"
    - "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-7 (induced by source paper "Analysis of hospital quality monitors using hierarchical time series models"):
  with members:
    - "STUDIES OF QUALITY MONITOR TIME SERIES: THE V.A. HOSPITAL SYSTEM build on foundational contributions in"

Connected through hyperedge HE-8 (induced by source paper "On Sequential Simulation-Based Methods for Bayesian Filtering"):
  with members:
    - "FILTERING VIA SIMULATION: AUXILIARY PARTICLE FILTERS"

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