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 "pmc gans generating multi scale high quality pedestrian with multimodal cascaded gans"):
  Abstract: Recently, generative adversarial networks (GANs) have shown great advantages in synthesizing images, leading to a boost of explorations of using faked images to augment data. This paper proposes a multimodal cascaded generative adversarial networks (PMC-GANs) to generate realistic and diversified pedestrian images and augment pedestrian detection data. The generator of our model applies a residual U-net structure, with multi-scale residual blocks to encode features, and attention residual blocks to help decode and rebuild pedestrian images. The model constructs in a coarse-to-fine fashion and adopts cascade structure, which is beneficial to produce high-resolution pedestrians. PMC-GANs outperforms baselines, and when used for data augmentation, it improves pedestrian detection results.

Includes members:
  Member "progressive pose attention transfer for person image generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "unsupervised person image generation with semantic parsing transformation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to transfer texture from clothing images to 3d humans")
  Member "learning to generate synthetic data via compositing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mixup regularization for region proposal based object detectors")
  Member "augmented cyclegan learning many to many mappings from unpaired data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "augmented cyclic consistency regularization for unpaired image to image translation")
  Member "occlusion aware r cnn detecting pedestrians in a crowd":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "image super resolution using very deep residual channel attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "pedestrian synthesis gan generating pedestrian data in real scene and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "harmonizing maximum likelihood with gans for multimodal conditional generation")
  Member "synthetic data augmentation using gan for improved liver lesion classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "imperfect imaganation implications of gans exacerbating biases on facial data augmentation and snapchat selfie lenses")

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)