human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "error bounds for approximations with deep relu networks"
  Abstract: We study expressive power of shallow and deep neural networks with piece-wise linear activation functions. We establish new rigorous upper and lower bounds for the network complexity in the setting of approximations in Sobolev spaces. In particular, we prove that deep ReLU networks more efficiently approximate smooth functions than shallow networks. In the case of approximations of 1D Lipschitz functions we describe adaptive depth-6 network architectures more efficient than the standard shallow architecture.

Connected through hyperedge HE-1 (induced by source paper "neural contextual bandits with ucb based exploration"):
  with members:
    - "how much over parameterization is sufficient to learn deep relu networks"
    - "an improved analysis of training over parameterized deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "deep neural linear bandits overcoming catastrophic forgetting through likelihood matching"
    - "on exact computation with an infinitely wide neural net"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "what can resnet learn efficiently going beyond kernels"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "statistical guarantees of generative adversarial networks for distribution estimation"):
  with members:
    - "width provably matters in optimization for deep linear neural networks"
    - "improving generalization and stability of generative adversarial networks"
    - "efficient approximation of deep relu networks for functions on low dimensional manifolds"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "improving gan training via binarized representation entropy bre regularization"

Connected through hyperedge HE-3 (induced by source paper "the gap between theory and practice in function approximation with deep neural networks"):
  with members:
    - "deep network approximation for smooth functions"
    - "the troublesome kernel why deep learning for inverse problems is typically unstable"
    - "greedy shallow networks a new approach for constructing and training neural networks"
    - "deep double descent where bigger models and more data hurt"
    - "growing axons greedy learning of neural networks with application to function approximation"
    - "a polynomial based approach for architectural design and learning with deep neural networks"
    - "on instabilities of deep learning in image reconstruction does ai come at a cost"
    - "neural network integral representations with the relu activation function"

Connected through hyperedge HE-4 (induced by source paper "a corrective view of neural networks representation memorization and learning"):
  with members:
    - "effect of activation functions on the training of overparametrized neural nets"
    - "quadratic suffices for over parametrization via matrix chernoff bound"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "barron spaces and the compositional function spaces for neural network models"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"
    - "on the power and limitations of random features for understanding neural networks"
    - "gradient descent finds global minima of deep neural networks"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-5 (induced by source paper "a novel learnable gradient descent type algorithm for non convex non smooth inverse problems"):
  with members:
    - "differentiable linearized admm"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "learning deep cnn denoiser prior for image restoration"
    - "the expressive power of neural networks a view from the width"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "learning the mapping mathbf x mapsto sum_ i 1 d x_i 2 the cost of finding the needle in a haystack"):
  with members:
    - "explaining landscape connectivity of low cost solutions for multilayer nets"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "barron spaces and the compositional function spaces for neural network models"
    - "a priori estimates of the population risk for residual networks"
    - "deep sets"
    - "rethinking the value of network pruning"
    - "expressive power of recurrent neural networks"
    - "learning functions when is deep better than shallow"

Connected through hyperedge HE-7 (induced by source paper "deep network approximation for smooth functions"):
  with members:
    - "nonlinear approximation via compositions"
    - "exponential convergence of the deep neural network approximation for analytic functions"
    - "construction of neural networks for realization of localized deep learning"
    - "universality of deep convolutional neural networks"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "optimal approximation with sparsely connected deep neural networks"
    - "the expressive power of neural networks a view from the width"

Connected through hyperedge HE-8 (induced by source paper "a closer look at the approximation capabilities of neural networks"):
  with members:
    - "on the power and limitations of random features for understanding neural networks"
    - "the expressive power of neural networks a view from the width"
    - "understanding deep neural networks with rectified linear units"
    - "the power of depth for feedforward neural networks"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.LG(Machine Learning)