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: "learning functions when is deep better than shallow"
  Abstract: While the universal approximation property holds both for hierarchical and shallow networks, we prove that deep (hierarchical) networks can approximate the class of compositional functions with the same accuracy as shallow networks but with exponentially lower number of training parameters as well as VC-dimension. This theorem settles an old conjecture by Bengio on the role of depth in networks. We then define a general class of scalable, shift-invariant algorithms to show a simple and natural set of requirements that justify deep convolutional networks.

Connected through hyperedge HE-1 (induced by source paper "learning the mapping mathbf x mapsto sum_ i 1 d x_i 2 the cost of finding the needle in a haystack"):
  with members:
    - "explaining landscape connectivity of low cost solutions for multilayer nets"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "barron spaces and the compositional function spaces for neural network models"
    - "a priori estimates of the population risk for residual networks"
    - "deep sets"
    - "rethinking the value of network pruning"
    - "expressive power of recurrent neural networks"
    - "error bounds for approximations with deep relu networks"

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"
    - "deep vs shallow networks an approximation theory perspective"
    - "neural architecture search with reinforcement learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "on the space time expressivity of resnets"):
  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"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "stable architectures for deep neural networks"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "why deep neural networks for function approximation"
    - "understanding deep neural networks with rectified linear units"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "polygan high order polynomial generators"):
  with members:
    - "emerging convolutions for generative normalizing flows"
    - "minimax estimation of neural net distance"
    - "spectral normalization for generative adversarial networks"
    - "generative adversarial networks an overview"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "deep neural networks for choice analysis architectural design with alternative specific utility functions"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "size independent sample complexity of neural networks"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-6 (induced by source paper "realizing data features by deep nets"):
  with members:
    - "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"
    - "the power of deeper networks for expressing natural functions"
    - "learning rates for classification with gaussian kernels"
    - "deep vs shallow networks an approximation theory perspective"
    - "depth width tradeoffs in approximating natural functions with neural networks"

Connected through hyperedge HE-7 (induced by source paper "limiting network size within finite bounds for optimization"):
  with members:
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "sharp minima can generalize for deep nets"
    - "spectrally normalized margin bounds for neural networks"
    - "size independent sample complexity of neural networks"
    - "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"

Connected through hyperedge HE-8 (induced by source paper "deep model reference adaptive control"):
  with members:
    - "generalization error in deep learning"
    - "understanding deep learning requires rethinking generalization"
    - "benefits of depth in neural networks"
    - "feature learning in deep neural networks studies on speech recognition tasks"
    - "robustness and generalization"

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)