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: "Priors for Infinite Networks"
  Abstract: Technical Report CRG-TR-94-1 Department of Computer Science University of Toronto 10 King's College Road Toronto, Canada M5S 1A4 Abstract Bayesian inference begins with a prior distribution for model parameters that is meant to capture prior beliefs about the relationship being modeled. For multilayer perceptron networks, where the parameters are the connection weights, the prior lacks any direct meaning | what matters is the prior over functions computed by the network that is implied by this prior over weights. In this paper, I show that priors over weights can be defined in such a way that the corresponding priors over functions reach reasonable limits as the number of hidden units in the network goes to infinity. When using such priors, there is thus no need to limit the size of the network in order to avoid "overfitting". The infinite network limit also provides insight into the properties of different priors. A Gaussian prior for hidden-to-output weights results in a Gaussian process prior for functions, which can be smooth, Brownian, or fractional Brownian, depending on the hidden unit activation function and the prior for input-to-hidden weights. Quite different effects can be obtained using priors based on non-Gaussian stable distributions. In networks with more than one hidden layer, a combination of Gaussian and non-Gaussian priors appears most interesting.

Connected through hyperedge HE-1 (induced by source paper "Bayesian Training of Backpropagation Networks by the Hybrid Monte Carlo Method"):
  with members:
    - "A Practical Bayesian Framework for Backprop Networks"
    - "5 Bayesian estimation 5.1 Introduction"

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"
    - "Probabilistic Networks: New Models and New Methods"
    - "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"
    - "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: Probabilistic_Methods