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: "An Improved Model for Spatially Correlated Binary Responses"
  Abstract: In this paper we extend the basic autologistic model to include covariates and an indication of sampling effort. The model is applied to sampled data instead of the traditional use for image analysis where complete data are available. We adopt a Bayesian set-up and develop a hybrid Gibbs sampling estimation procedure. Using simulated examples, we show that the autologistic model with covariates for sample data improves predictions as compared to the simple logistic regression model and the standard autologistic model (without covariates).

Connected through hyperedge HE-1 (induced by source paper "Bayesian Model Averaging"):
  with members:
    - "Why Does Bagging Work? A Bayesian Account and its Implications bagging's success, both in a"

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