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: "Hierarchical Spatio-Temporal Mapping of Disease Rates"
  Abstract: Maps of regional morbidity and mortality rates are useful tools in determining spatial patterns of disease. Combined with socio-demographic census information, they also permit assessment of environmental justice, i.e., whether certain subgroups suffer disproportionately from certain diseases or other adverse effects of harmful environmental exposures. Bayes and empirical Bayes methods have proven useful in smoothing crude maps of disease risk, eliminating the instability of estimates in low-population areas while maintaining geographic resolution. In this paper we extend existing hierarchical spatial models to account for temporal effects and spatio-temporal interactions. Fitting the resulting highly-parametrized models requires careful implementation of Markov chain Monte Carlo (MCMC) methods, as well as novel techniques for model evaluation and selection. We illustrate our approach using a dataset of county-specific lung cancer rates in the state of Ohio during the period 1968-1988.

Connected through hyperedge HE-1 (induced by source paper "Modelling Risk from a Disease in Time and Space"):
  with members:
    - "Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models"
    - "Bayesian Analysis of Agricultural Field Experiments"

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