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 "approximation smooth and sparse functions by deep neural networks without saturation"):
  Abstract: Constructing neural networks for function approximation is a classical and longstanding topic in approximation theory. In this paper, we aim at constructing deep neural networks (deep nets for short) with three hidden layers to approximate smooth and sparse functions. In particular, we prove that the constructed deep nets can reach the optimal approximation rate in approximating both smooth and sparse functions with controllable magnitude of free parameters. Since the saturation that describes the bottleneck of approximate is an insurmountable problem of constructive neural networks, we also prove that deepening the neural network with only one more hidden layer can avoid the saturation. The obtained results underlie advantages of deep nets and provide theoretical explanations for deep learning.

Includes members:
  Member "realizing data features by deep nets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "realization of spatial sparseness by deep relu nets with massive data")
  Member "deep neural networks for rotation invariance approximation and learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "realization of spatial sparseness by deep relu nets with massive data")
  Member "generalization and expressivity for deep nets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "realization of spatial sparseness by deep relu nets with massive data")
  Member "construction of neural networks for realization of localized deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep network approximation for smooth functions")
  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 vs shallow networks an approximation theory perspective":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "understanding generalization in deep learning via tensor methods")
  Member "benefits of depth in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lossless compression of deep neural networks")
  Member "constructive neural network learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "highlight every step knowledge distillation via collaborative teaching")

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.IT(Information Theory)