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: "exponential convergence of the deep neural network approximation for analytic functions"
  Abstract: We prove that for analytic functions in low dimension, the convergence rate of the deep neural network approximation is exponential.

Connected through hyperedge HE-1 (induced by source paper "deep network approximation for smooth functions"):
  with members:
    - "nonlinear approximation via compositions"
    - "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"
    - "error bounds for approximations with deep relu networks"

Connected through hyperedge HE-2 (induced by source paper "mgnet a unified framework of multigrid and convolutional neural network"):
  with members:
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "learning neural pde solvers with convergence guarantees"
    - "universality of deep convolutional neural networks"
    - "a flow model of neural networks"
    - "learning across scales a multiscale method for convolution neural networks"
    - "the reversible residual network backpropagation without storing activations"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "dual path networks"

Connected through hyperedge HE-3 (induced by source paper "a priori estimates of the population risk for residual networks"):
  with members:
    - "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"
    - "capacity control of relu neural networks by basis path norm"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "fisher rao metric geometry and complexity of neural networks"

Connected through hyperedge HE-4 (induced by source paper "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"):
  with members:
    - "deep fluids a generative network for parameterized fluid simulations"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "extracting structured dynamical systems using sparse optimization with very few samples"
    - "stable architectures for deep neural networks"
    - "model reduction with memory and the machine learning of dynamical systems"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "neural ordinary differential equations"
    - "learning equations for extrapolation and control"

Connected through hyperedge HE-5 (induced by source paper "chebnet efficient and stable constructions of deep neural networks with rectified power units using chebyshev approximations"):
  with members:
    - "powernet efficient representations of polynomials and smooth functions by deep neural networks with rectified power units"
    - "error bounds for approximations with deep relu networks"
    - "benefits of depth in neural networks"
    - "why deep neural networks for function approximation"
    - "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
    - "the power of depth for feedforward neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "nonlinear approximation via compositions"):
  with members:
    - "optimal approximation of continuous functions by very deep relu networks"
    - "the loss surface of deep and wide neural networks"
    - "the expressive power of neural networks a view from the width"
    - "error bounds for approximations with deep relu networks"
    - "adam a method for stochastic optimization"
    - "nonlinear basis pursuit"

Connected through hyperedge HE-7 (induced by source paper "nonlinear approximation and deep relu networks"):
  with members:
    - "quantified advantage of discontinuous weight selection in approximations with deep neural networks"
    - "why deep neural networks for function approximation"
    - "representation benefits of deep feedforward networks"

Connected through hyperedge HE-8 (induced by source paper "powernet efficient representations of polynomials and smooth functions by deep neural networks with rectified power units"):
  with members:
    - "error bounds for approximations with deep relu networks"
    - "understanding deep neural networks with rectified linear units"

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)