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 "lightweight residual densely connected convolutional neural network"):
  Abstract: Extremely efficient convolutional neural network architectures are one of the most important requirements for limited computing power devices (such as embedded and mobile devices). Recently, some architectures have been proposed to overcome this limitation by considering specific hardware-software equipment. In this paper, the residual densely connected blocks are proposed to guaranty the deep supervision, efficient gradient flow, and feature reuse abilities of convolutional neural network. The proposed method decreases the cost of training and inference processes without using any special hardware-software equipment by just reducing the number of parameters and computational operations while achieving a feasible accuracy. Extensive experimental results demonstrate that the proposed architecture is more efficient than the AlexNet and VGGNet in terms of model size, required parameters, and even accuracy. The proposed model is evaluated on the ImageNet, MNIST, Fashion MNIST, SVHN, CIFAR-10, and CIFAR-100. It achieves state-of-the-art results on the Fashion MNIST dataset and reasonable results on the others. The obtained results show that the proposed model is superior to efficient models such as the SqueezNet and is also comparable with the state-of-the-art efficient models such as CondenseNet and ShuffleNet.

Includes members:
  Member "denser deep evolutionary network structured representation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automatic design of artificial neural networks for gamma ray detection")
  Member "indoor scene understanding in 2 5 3d for autonomous agents a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "resolving 3d human pose ambiguities with 3d scene constraints")
  Member "residual dense network for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "convolutional neural networks with alternately updated clique":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "context gated convolution")
  Member "competitive inner imaging squeeze and excitation for residual network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stochastic region pooling make attention more expressive")
  Member "residual dense network for image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather")
  Member "skipnet learning dynamic routing in convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "switchable precision neural networks")
  Member "shufflenet an extremely efficient convolutional neural network for mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "post training piecewise linear quantization for deep neural networks")

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.CV(Computer Vision and Pattern Recognition)