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: "Regression with Input-dependent Noise: A Gaussian Process Treatment"
  Abstract: Technical Report NCRG/98/002, available from http://www.ncrg.aston.ac.uk/ To appear in Advances in Neural Information Processing Systems 10 eds. M. I. Jordan, M. J. Kearns and S. A. Solla. Lawrence Erlbaum (1998). Abstract Gaussian processes provide natural non-parametric prior distributions over regression functions. In this paper we consider regression problems where there is noise on the output, and the variance of the noise depends on the inputs. If we assume that the noise is a smooth function of the inputs, then it is natural to model the noise variance using a second Gaussian process, in addition to the Gaussian process governing the noise-free output value. We show that prior uncertainty about the parameters controlling both processes can be handled and that the posterior distribution of the noise rate can be sampled from using Markov chain Monte Carlo methods. Our results on a synthetic data set give a posterior noise variance that well-approximates the true variance.

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

Connected through hyperedge HE-2 (induced by source paper "Probabilistic Networks: New Models and New Methods"):
  with members:
    - "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"
    - "A Practical Bayesian Framework for Backprop Networks"
    - "Interpolation Models with Multiple"
    - "Bayesian Non-linear Modelling for the Prediction Competition"

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