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 "generating object stamps"):
  Abstract: We present an algorithm to generate diverse foreground objects and composite them into background images using a GAN architecture. Given an object class, a user-provided bounding box, and a background image, we first use a mask generator to create an object shape, and then use a texture generator to fill the mask such that the texture integrates with the background. By separating the problem of object insertion into these two stages, we show that our model allows us to improve the realism of diverse object generation that also agrees with the provided background image. Our results on the challenging COCO dataset show improved overall quality and diversity compared to state-of-the-art object insertion approaches.

Includes members:
  Member "transgaga geometry aware unsupervised image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "augmented cyclic consistency regularization for unpaired image to image translation")
  Member "adaptive composition gan towards realistic image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ga dan geometry aware domain adaptation network for scene text detection and recognition")
  Member "semantic image synthesis with spatially adaptive normalization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "st gan spatial transformer generative adversarial networks for image compositing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on feature normalization and data augmentation")
  Member "multimodal unsupervised image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object centric image generation from layouts")
  Member "the unreasonable effectiveness of deep features as a perceptual metric":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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)