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: "Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo"
  Abstract: The full Bayesian method for applying neural networks to a prediction problem is to set up the prior/hyperprior structure for the net and then perform the necessary integrals. However, these integrals are not tractable analytically, and Markov Chain Monte Carlo (MCMC) methods are slow, especially if the parameter space is high-dimensional. Using Gaussian processes we can approximate the weight space integral analytically, so that only a small number of hyperparameters need be integrated over by MCMC methods. We have applied this idea to classification problems, obtaining ex cellent results on the real-world problems investigated so far.

Connected through hyperedge HE-1 (induced by source paper "Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification"):
  with members:
    - "Regression with Input-dependent Noise: A Gaussian Process Treatment"
    - "Efficient Implementation of Gaussian Processes"
    - "Variational Gaussian Process Classifiers"

Connected through hyperedge HE-2 (induced by source paper "Prior Information and Generalized Questions"):
  with members:
    - "PRIOR KNOWLEDGE AND THE CREATION OF "VIRTUAL" EXAMPLES FOR RBF NETWORKS 1"
    - "Regularization Theory and Neural Networks Architectures"

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: Neural_Networks