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 Analysis of Agricultural Field Experiments"
  Abstract: SUMMARY The paper describes Bayesian analysis for agricultural field experiments, a topic that has received very little previous attention, despite a vast frequentist literature. Adoption of the Bayesian paradigm simplifies the interpretation of the results, especially in ranking and selection. Also, complex formulations can be analyzed with comparative ease, using Markov chain Monte Carlo methods. A key ingredient in the approach is the need for spatial representations of the unobserved fertility patterns. This is discussed in detail. Problems caused by outliers and by jumps in fertility are tackled via hierarchical-t formulations that may find use in other contexts. The paper includes three analyses of variety trials for yield and one example involving binary data; none is entirely straightforward. Some comparisons with frequentist analyses are made. The datasets are available at http://www.stat.duke.edu/~higdon/trials/data.html.

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"
    - "Hierarchical Spatio-Temporal Mapping of Disease Rates"

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