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 "semantics aware image to image translation and domain transfer"):
  Abstract: Image to image translation is the problem of transferring an image from a source domain to a target domain. We present a new method to transfer the underlying semantics of an image even when there are geometric changes across the two domains. Specifically, we present a Generative Adversarial Network (GAN) that can transfer semantic information presented as segmentation masks. Our main technical contribution is an encoder-decoder based generator architecture that jointly encodes the image and its underlying semantics and translates both simultaneously to the target domain. Additionally, we propose object transfiguration and cross-domain semantic consistency losses that preserve the underlying semantic labels maps. We demonstrate the effectiveness of our approach in multiple object transfiguration and domain transfer tasks through qualitative and quantitative experiments. The results show that our method is better at transferring image semantics than state of the art image to image translation methods.

Includes members:
  Member "travelgan image to image translation by transformation vector learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reusing discriminators for encoding towards unsupervised image to image translation")
  Member "harmonic unpaired image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning a self inverse network for unpaired bidirectional image to image translation")
  Member "art2real unfolding the reality of artworks via semantically aware image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "semantic aware grad gan for virtual to real urban scene adaption":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "can giraffes become birds an evaluation of image to image translation for data generation")
  Member "sem gan semantically consistent image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lc gan image to image translation based on generative adversarial network for endoscopic images")
  Member "image to image translation for cross domain disentanglement":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to adapt class specific features across domains for semantic segmentation")
  Member "image to image translation for domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to adapt class specific features across domains for semantic segmentation")
  Member "cycada cycle consistent adversarial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a novel framework for selection of gans for an application")

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)