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: "deep neural network approximation theory"
  Abstract: Deep neural networks have become state-of-the-art technology for a wide range of practical machine learning tasks such as image classification, handwritten digit recognition, speech recognition, or game intelligence. This paper develops the fundamental limits of learning in deep neural networks by characterizing what is possible if no constraints on the learning algorithm and the amount of training data are imposed. Concretely, we consider information-theoretically optimal approximation through deep neural networks with the guiding theme being a relation between the complexity of the function (class) to be approximated and the complexity of the approximating network in terms of connectivity and memory requirements for storing the network topology and the associated quantized weights. The theory we develop educes remarkable universality properties of deep networks. Specifically, deep networks are optimal approximants for vastly different function classes such as affine systems and Gabor systems. This universality is afforded by a concurrent invariance property of deep networks to time-shifts, scalings, and frequency-shifts. In addition, deep networks provide exponential approximation accuracy i.e., the approximation error decays exponentially in the number of non-zero weights in the network of vastly different functions such as the squaring operation, multiplication, polynomials, sinusoidal functions, general smooth functions, and even one-dimensional oscillatory textures and fractal functions such as the Weierstrass function, both of which do not have any known methods achieving exponential approximation accuracy. In summary, deep neural networks provide information-theoretically optimal approximation of a very wide range of functions and function classes used in mathematical signal processing.

Connected through hyperedge HE-1 (induced by source paper "robust and resource efficient identification of two hidden layer neural networks"):
  with members:
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "size independent sample complexity of neural networks"
    - "energy propagation in deep convolutional neural networks"
    - "towards evaluating the robustness of neural networks"
    - "finding a low rank basis in a matrix subspace"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "breaking the curse of dimensionality with convex neural networks"

Connected through hyperedge HE-2 (induced by source paper "expression of fractals through neural network functions"):
  with members:
    - "nonlinear approximation and deep relu networks"
    - "bounding and counting linear regions of deep neural networks"
    - "error bounds for approximations with deep relu networks"
    - "why deep neural networks for function approximation"
    - "understanding deep neural networks with rectified linear units"
    - "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
    - "the power of depth for feedforward neural networks"
    - "on the expressive power of deep learning a tensor analysis"

Connected through hyperedge HE-3 (induced by source paper "learning optimal solutions for extremely fast ac optimal power flow"):
  with members:
    - "learning for dc opf classifying active sets using neural nets"
    - "deepopf deep neural network for dc optimal power flow"
    - "review of smart meter data analytics applications methodologies and challenges"
    - "machine learning for ac optimal power flow"
    - "machine learning in the air"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "towards a regularity theory for relu networks chain rule and global error estimates"):
  with members:
    - "analysis of the generalization error empirical risk minimization over deep artificial neural networks overcomes the curse of dimensionality in the numerical approximation of black scholes partial differential equations"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "the deep ritz method a deep learning based numerical algorithm for solving variational problems"

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)