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: "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.

Connected through hyperedge HE-1 (induced by source paper "Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo"):
  with members:
    - "Prior Information and Generalized Questions"

Connected through hyperedge HE-2 (induced by source paper "Efficient Implementation of Gaussian Processes"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "A Practical Bayesian Framework for Backprop Networks"

Connected through hyperedge HE-3 (induced by source paper "Regression with Input-dependent Noise: A Gaussian Process Treatment"):
  with members:
    - "Probabilistic Networks: New Models and New Methods"

Connected through hyperedge HE-4 (induced by source paper "Variational Gaussian Process Classifiers"):
  with members:
    (none)

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