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: "Probabilistic Networks: New Models and New Methods"
  Abstract: In this paper I describe the implementation of a probabilistic regression model in BUGS. BUGS is a program that carries out Bayesian inference on statistical problems using a simulation technique known as Gibbs sampling. It is possible to implement surprisingly complex regression models in this environment. I demonstrate the simultaneous inference of an interpolant and an input-dependent noise level.

Connected through hyperedge HE-1 (induced by source paper "Interpolation Models with Multiple"):
  with members:
    - "Bayesian Non-linear Modelling for the Prediction Competition"

Connected through hyperedge HE-2 (induced by source paper "A Practical Bayesian Framework for Backprop Networks"):
  with members:
    - "Density Networks and their Application to Protein Modelling"
    - "PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS"
    - "FLAT MINIMA Neural Computation 9(1):1-42 (1997)"
    - "Using Neural Networks for Descriptive Statistical Analysis of Educational Data"
    - "Ensemble Learning and Evidence Maximization"
    - "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"
    - "Bayesian Non-linear Modelling for the Prediction Competition"
    - "MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III)"

Connected through hyperedge HE-3 (induced by source paper "Regression with Input-dependent Noise: A Gaussian Process Treatment"):
  with members:
    - "Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification"

Connected through hyperedge HE-4 (induced by source paper "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"):
  with members:
    - "FLAT MINIMA Neural Computation 9(1):1-42 (1997)"
    - "Ensemble Learning and Evidence Maximization"
    - "Ensemble Learning for Hidden Markov Models"
    - "Developments in Probabilistic Modelling with Neural Networks|Ensemble Learning"
    - "A Practical Bayesian Framework for Backprop Networks"

Connected through hyperedge HE-5 (induced by source paper "Bayesian Non-linear Modelling for the Prediction Competition"):
  with members:
    - "A Practical Bayesian Framework for Backprop Networks"
    - "Interpolation Models with Multiple"

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: Theory