human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "realization of spatial sparseness by deep relu nets with massive data"):
  Abstract: The great success of deep learning poses urgent challenges for understanding its working mechanism and rationality. The depth, structure, and massive size of the data are recognized to be three key ingredients for deep learning. Most of the recent theoretical studies for deep learning focus on the necessity and advantages of depth and structures of neural networks. In this paper, we aim at rigorous verification of the importance of massive data in embodying the out-performance of deep learning. To approximate and learn spatially sparse and smooth functions, we establish a novel sampling theorem in learning theory to show the necessity of massive data. We then prove that implementing the classical empirical risk minimization on some deep nets facilitates in realization of the optimal learning rates derived in the sampling theorem. This perhaps explains why deep learning performs so well in the era of big data.

Includes members:
  Member "realizing data features by deep nets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximation smooth and sparse functions by deep neural networks without saturation")
  Member "deep neural networks for rotation invariance approximation and learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximation smooth and sparse functions by deep neural networks without saturation")
  Member "generalization and expressivity for deep nets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximation smooth and sparse functions by deep neural networks without saturation")
  Member "construction of neural networks for realization of localized deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximation smooth and sparse functions by deep neural networks without saturation")
  Member "universality of deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "deep radial kernel networks approximating radially symmetric functions with deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "realizing data features by deep nets")
  Member "deep vs shallow networks an approximation theory perspective":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "understanding generalization in deep learning via tensor methods")
  Member "distributed learning with regularized least squares":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generalised lipschitz regularisation equals distributional robustness")

Question: Please tell me which class the source paper 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)