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 "realizing data features by deep nets"):
  Abstract: This paper considers the power of deep neural networks (deep nets for short) in realizing data features. Based on refined covering number estimates, we find that, to realize some complex data features, deep nets can improve the performances of shallow neural networks (shallow nets for short) without requiring additional capacity costs. This verifies the advantage of deep nets in realizing complex features. On the other hand, to realize some simple data feature like the smoothness, we prove that, up to a logarithmic factor, the approximation rate of deep nets is asymptotically identical to that of shallow nets, provided that the depth is fixed. This exhibits a limitation of deep nets in realizing simple features.

Includes members:
  Member "generalization and expressivity for 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 "construction of neural networks for realization of localized deep 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 "universality of deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "realization of spatial sparseness by deep relu nets with massive data")
  Member "the power of deeper networks for expressing natural functions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "learning rates for classification with gaussian kernels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast polynomial kernel classification for massive data")
  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 "learning functions when is deep better than shallow":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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")

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)