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 vs shallow networks an approximation theory perspective"
  Abstract: The paper briefy reviews several recent results on hierarchical architectures for learning from examples, that may formally explain the conditions under which Deep Convolutional Neural Networks perform much better in function approximation problems than shallow, one-hidden layer architectures. The paper announces new results for a non-smooth activation function - the ReLU function - used in present-day neural networks, as well as for the Gaussian networks. We propose a new definition of relative dimension to encapsulate different notions of sparsity of a function class that can possibly be exploited by deep networks but not by shallow ones to drastically reduce the complexity required for approximation and learning.

Connected through hyperedge HE-1 (induced by source paper "understanding generalization in deep learning via tensor methods"):
  with members:
    - "the singular values of convolutional layers"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "generalization error in deep learning"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "tensor contraction layers for parsimonious deep nets"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-2 (induced by source paper "impact of fully connected layers on performance of convolutional neural networks for image classification"):
  with members:
    - "rccnet an efficient convolutional neural network for histological routine colon cancer nuclei classification"
    - "efficient neural architecture search via parameter sharing"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "the expressive power of neural networks a view from the width"
    - "mask r cnn"
    - "learning functions when is deep better than shallow"
    - "neural architecture search with reinforcement learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "approximation smooth and sparse functions by deep neural networks without saturation"):
  with members:
    - "realizing data features by deep nets"
    - "deep neural networks for rotation invariance approximation and learning"
    - "generalization and expressivity for deep nets"
    - "construction of neural networks for realization of localized deep learning"
    - "universality of deep convolutional neural networks"
    - "benefits of depth in neural networks"
    - "constructive neural network learning"
    - "the power of depth for feedforward neural networks"

Connected through hyperedge HE-4 (induced by source paper "the local elasticity of neural networks"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "barron spaces and the compositional function spaces for neural network models"
    - "implicit regularization in deep matrix factorization"
    - "efficient approximation of deep relu networks for functions on low dimensional manifolds"
    - "stiffness a new perspective on generalization in neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"

Connected through hyperedge HE-5 (induced by source paper "stability and generalization of graph convolutional neural networks"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "how powerful are graph neural networks"
    - "exploring graph structured passage representation for multi hop reading comprehension with graph neural networks"
    - "covariant compositional networks for learning graphs"
    - "unifying local and non local signal processing with graph cnns"
    - "deriving neural architectures from sequence and graph kernels"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-6 (induced by source paper "mean field limit of the learning dynamics of multilayer neural networks"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "small nonlinearities in activation functions create bad local minima in 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"
    - "the loss landscape of overparameterized neural networks"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"

Connected through hyperedge HE-7 (induced by source paper "enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting"):
  with members:
    - "generating long sequences with sparse transformers"
    - "generating wikipedia by summarizing long sequences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "attention is all you need"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "condensenet an efficient densenet using learned group convolutions"

Connected through hyperedge HE-8 (induced by source paper "optimal function approximation with relu neural networks"):
  with members:
    - "universality of deep convolutional neural networks"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "expressive power of recurrent neural networks"
    - "the power of deeper networks for expressing natural functions"
    - "the expressive power of neural networks a view from the width"
    - "error bounds for approximations with deep relu networks"
    - "benefits of depth in 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)