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: "Robust Interpretation of Neural-Network Models"
  Abstract: Artificial Neural Network seem very promising for regression and classification, especially for large covariate spaces. These methods represent a non-linear function as a composition of low dimensional ridge functions and therefore appear to be less sensitive to the dimensionality of the covariate space. However, due to non uniqueness of a global minimum and the existence of (possibly) many local minima, the model revealed by the network is non stable. We introduce a method to interpret neural network results which uses novel robustification techniques. This results in a robust interpretation of the model employed by the network. Simulated data from known models is used to demonstrate the interpretability results and to demonstrate the effects of different regularization methods on the robustness of the model. Graphical methods are introduced to present the interpretation results. We further demonstrate how interaction between covariates can be revealed. From this study we conclude that the interpretation method works well, but that NN models may sometimes be misinterpreted, especially if the approximations to the true model are less robust.

Connected through hyperedge HE-1 (induced by source paper "Bootstrapping with Noise: An Effective Regularization Technique"):
  with members:
    (none)

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