human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification"):
  Abstract: Technical Report No. 9702, Department of Statistics, University of Toronto Abstract. Gaussian processes are a natural way of defining prior distributions over functions of one or more input variables. In a simple nonparametric regression problem, where such a function gives the mean of a Gaussian distribution for an observed response, a Gaussian process model can easily be implemented using matrix computations that are feasible for datasets of up to about a thousand cases. Hyperparameters that define the covariance function of the Gaussian process can be sampled using Markov chain methods. Regression models where the noise has a t distribution and logistic or probit models for classification applications can be implemented by sampling as well for latent values underlying the observations. Software is now available that implements these methods using covariance functions with hierarchical parameterizations. Models defined in this way can discover high-level properties of the data, such as which inputs are relevant to predicting the response.

Includes members:
  Member "Regression with Input-dependent Noise: A Gaussian Process Treatment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Probabilistic Networks: New Models and New Methods")
  Member "Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Prior Information and Generalized Questions")
  Member "Efficient Implementation of Gaussian Processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning networks for face analysis and synthesis")
  Member "Variational Gaussian Process Classifiers":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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