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: "Interpolation Models with Multiple"
  Abstract: A traditional interpolation model is characterized by the choice of reg-ularizer applied to the interpolant, and the choice of noise model. Typically, the regularizer has a single regularization constant ff, and the noise model has a single parameter fi. The ratio ff=fi alone is responsible for determining globally all these attributes of the interpolant: its `complexity', `flexibility', `smoothness', `characteristic scale length', and `characteristic amplitude'. We suggest that interpolation models should be able to capture more than just one flavour of simplicity and complexity. We describe Bayesian models in which the interpolant has a smoothness that varies spatially. We emphasize the importance, in practical implementation, of the concept of `conditional convexity' when designing models with many hyperparameters. We apply the new models to the interpolation of neuronal spike data and demonstrate a substantial improvement in generalization error.

Connected through hyperedge HE-1 (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"
    - "Regression with Input-dependent Noise: A Gaussian Process Treatment"
    - "Bayesian Non-linear Modelling for the Prediction Competition"

Connected through hyperedge HE-2 (induced by source paper "Bayesian Non-linear Modelling for the Prediction Competition"):
  with members:
    - "Probabilistic Networks: New Models and New Methods"
    - "A Practical Bayesian Framework for Backprop Networks"

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