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 "improve object detection by data enhancement based on generative adversarial nets"):
  Abstract: The accuracy of the object detection model depends on whether the anchor boxes effectively trained. Because of the small number of GT boxes or object target is invariant in the training phase, cannot effectively train anchor boxes. Improving detection accuracy by extending the dataset is an effective way. We propose a data enhancement method based on the foreground-background separation model. While this model uses a binary image of object target random perturb original dataset image. Perturbation methods include changing the color channel of the object, adding salt noise to the object, and enhancing contrast. The main contribution of this paper is to propose a data enhancement method based on GAN and improve detection accuracy of DSSD. Results are shown on both PASCAL VOC2007 and PASCAL VOC2012 dataset. Our model with 321x321 input achieves 78.7% mAP on the VOC2007 test, 76.6% mAP on the VOC2012 test.

Includes members:
  Member "pseudo mask augmented object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic graph convolutional networks for 3d human pose regression")
  Member "perceptual adversarial networks for image to image transformation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "medgan medical image translation using gans")
  Member "dssd deconvolutional single shot detector":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "image to image translation with conditional adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "spatial pyramid pooling in deep convolutional networks for visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

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)