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: "depth width tradeoffs in approximating natural functions with neural networks"
  Abstract: We provide several new depth-based separation results for feed-forward neural networks, proving that various types of simple and natural functions can be better approximated using deeper networks than shallower ones, even if the shallower networks are much larger. This includes indicators of balls and ellipses; non-linear functions which are radial with respect to the $L_1$ norm; and smooth non-linear functions. We also show that these gaps can be observed experimentally: Increasing the depth indeed allows better learning than increasing width, when training neural networks to learn an indicator of a unit ball.

Connected through hyperedge HE-1 (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-2 (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"
    - "deep vs shallow networks an approximation theory perspective"

Connected through hyperedge HE-3 (induced by source paper "when can neural networks learn connected decision regions"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "learning relus via gradient descent"
    - "optimization landscape and expressivity of deep cnns"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "the loss surface of deep and wide neural networks"
    - "the expressive power of neural networks a view from the width"
    - "theory ii landscape of the empirical risk in deep learning"
    - "error bounds for approximations with deep relu networks"

Connected through hyperedge HE-4 (induced by source paper "the oracle of dlphi"):
  with members:
    - "nfft meets krylov methods fast matrix vector products for the graph laplacian of fully connected networks"
    - "a haar wavelet based perceptual similarity index for image quality assessment"
    - "adversarial regularizers in inverse problems"
    - "the universal approximation power of finite width deep relu networks"
    - "mathematics of deep learning"
    - "underwater object classification using scattering transform of sonar signals"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "error bounds for approximations with deep relu networks"

Connected through hyperedge HE-5 (induced by source paper "realization of spatial sparseness by deep relu nets with massive data"):
  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"
    - "deep radial kernel networks approximating radially symmetric functions with deep networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "distributed learning with regularized least squares"

Connected through hyperedge HE-6 (induced by source paper "a function space view of bounded norm infinite width relu nets the multivariate case"):
  with members:
    - "two models of double descent for weak features"
    - "approximation power of random neural networks"
    - "how do infinite width bounded norm networks look in function space"
    - "gradient descent maximizes the margin of homogeneous neural networks"
    - "neural tangent kernels transportation mappings and universal approximation"
    - "neural network with unbounded activation functions is universal approximator"
    - "error bounds for approximations with deep relu networks"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-7 (induced by source paper "function approximation by deep networks"):
  with members:
    - "dimension independent bounds for general shallow networks"
    - "construction of neural networks for realization of localized deep learning"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "on the expressive power of overlapping architectures of deep learning"
    - "function approximation with zonal function networks with activation functions analogous to the rectified linear unit functions"
    - "bounding and counting linear regions of deep neural networks"
    - "error bounds for approximations with deep relu networks"
    - "deep vs shallow networks an approximation theory perspective"

Connected through hyperedge HE-8 (induced by source paper "depth separations in neural networks what is actually being separated"):
  with members:
    - "provable limitations of deep learning"
    - "on the complexity of learning neural networks"
    - "failures of gradient based deep learning"
    - "depth separation for neural networks"
    - "error bounds for approximations with deep relu networks"
    - "distribution specific hardness of learning neural networks"
    - "benefits of depth in neural networks"
    - "why deep neural networks for function approximation"

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)