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: "FILTERING VIA SIMULATION: AUXILIARY PARTICLE FILTERS"
  Abstract: This paper analyses the recently suggested particle approach to filtering time series. We suggest that the algorithm is not robust to outliers for two reasons: the design of the simulators and the use of the discrete support to represent the sequentially updating prior distribution. Both problems are tackled in this paper. We believe we have largely solved the first problem and have reduced the order of magnitude of the second. In addition we introduce the idea of stratification into the particle filter which allows us to perform on-line Bayesian calculations about the parameters which index the models and maximum likelihood estimation. The new methods are illustrated by using a stochastic volatility model and a time series model of angles.

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"
    - "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-2 (induced by source paper "On Sequential Simulation-Based Methods for Bayesian Filtering"):
  with members:
    - "Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models"

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