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: "STUDIES OF QUALITY MONITOR TIME SERIES: THE V.A. HOSPITAL SYSTEM build on foundational contributions in"
  Abstract: This report describes statistical research and development work on hospital quality monitor data sets from the nationwide VA hospital system. The project covers statistical analysis, exploration and modelling of data from several quality monitors, with the primary goals of: (a) understanding patterns of variability over time in hospital-level and monitor area specific quality monitor measures, and (b) understanding patterns of dependencies between sets of monitors. We present discussion of basic perspectives on data structure and preliminary data exploration for three monitors, followed by developments of several classes of formal models. We identify classes of hierarchical random effects time series models to be of relevance in mod-elling single or multiple monitor time series. We summarise basic model features and results of analyses of the three monitor data sets, in both single and multiple monitor frameworks, and present a variety of summary inferences in graphical displays. Our discussion includes summary conclusions related to the two key goals, discussions of questions of comparisons across hospitals, and some recommendations about further potential substantive and statistical investigations.

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "Analysis of hospital quality monitors using hierarchical time series models"):
  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