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: "universality of deep convolutional neural networks"
  Abstract: Deep learning has been widely applied and brought breakthroughs in speech recognition, computer vision, and many other domains. The involved deep neural network architectures and computational issues have been well studied in machine learning. But there lacks a theoretical foundation for understanding the approximation or generalization ability of deep learning methods generated by the network architectures such as deep convolutional neural networks having convolutional structures. Here we show that a deep convolutional neural network (CNN) is universal, meaning that it can be used to approximate any continuous function to an arbitrary accuracy when the depth of the neural network is large enough. This answers an open question in learning theory. Our quantitative estimate, given tightly in terms of the number of free parameters to be computed, verifies the efficiency of deep CNNs in dealing with large dimensional data. Our study also demonstrates the role of convolutions in deep CNNs.

Connected through hyperedge HE-1 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming"):
  with members:
    - "learning to detect"
    - "learning optimal resource allocations in wireless systems"
    - "power control in cellular massive mimo with varying user activity a deep learning solution"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "cnn based signal detection for banded linear systems"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "deep learning for radio resource allocation in multi cell networks"

Connected through hyperedge HE-2 (induced by source paper "convolutional spectral kernel learning"):
  with members:
    - "automated spectral kernel learning"
    - "orthogonal deep neural networks"
    - "functional variational bayesian neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "differentiable compositional kernel learning for gaussian processes"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-3 (induced by source paper "nonparametric regression on low dimensional manifolds using deep relu networks"):
  with members:
    - "nonlinear approximation and deep relu networks"
    - "efficient approximation of deep relu networks for functions on low dimensional manifolds"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "squeeze and excitation networks"
    - "recent trends in deep learning based natural language processing"
    - "the expressive power of neural networks a view from the width"
    - "deep nets for local manifold learning"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"

Connected through hyperedge HE-4 (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"
    - "deep vs shallow networks an approximation theory perspective"
    - "benefits of depth in neural networks"
    - "constructive neural network learning"
    - "the power of depth for feedforward neural networks"

Connected through hyperedge HE-5 (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"
    - "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-6 (induced by source paper "graph neural networks exponentially lose expressive power for node classification"):
  with members:
    - "optuna a next generation hyperparameter optimization framework"
    - "break the ceiling stronger multi scale deep graph convolutional networks"
    - "on the universality of invariant networks"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "simplifying graph convolutional networks"
    - "stability and generalization of graph convolutional neural networks"
    - "a comprehensive survey on graph neural networks"
    - "deepgcns can gcns go as deep as cnns"

Connected through hyperedge HE-7 (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"
    - "exponential convergence of the deep neural network approximation for analytic functions"
    - "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-8 (induced by source paper "hark side of deep learning from grad student descent to automated machine learning"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "evaluating the search phase of neural architecture search"
    - "do imagenet classifiers generalize to imagenet"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "ngraph he a graph compiler for deep learning on homomorphically encrypted data"
    - "regularized evolution for image classifier architecture search"
    - "universal approximations of invariant maps by neural networks"
    - "do cifar 10 classifiers generalize to cifar 10"

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)