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 "unified generative adversarial networks for controllable image to image translation"):
  Abstract: Controllable image-to-image translation, i.e., transferring an image from a source domain to a target one guided by controllable structures, has attracted much attention in both academia and industry. In this paper, we propose a unified Generative Adversarial Network (GAN) framework for controllable image-to-image translation. In addition to conditioning on a reference image, we show how the model can generate images conditioned on controllable structures, e.g., class labels, object keypoints, human skeletons and scene semantic maps. The proposed GAN framework consists of a single generator and a discriminator taking a conditional image and the target controllable structure as input. In this way, the conditional image can provide appearance information and the controllable structure can provide the structure information for generating the target result. Moreover, the proposed GAN learns the image-to-image mapping through three novel losses, i.e., color loss, controllable structure-guided cycle-consistency loss and controllable structure-guided self-identity preserving loss. Note that the proposed color loss handles the issue of "channel pollution" when back-propagating the gradients. In addition, we present the Fr\'echet ResNet Distance (FRD) to evaluate the quality of generated images. Extensive qualitative and quantitative experiments on two challenging image translation tasks with four different datasets demonstrate that the proposed GAN model generates convincing results, and significantly outperforms other state-of-the-art methods on both tasks. Meanwhile, the proposed GAN framework is a unified solution, thus it can be applied to solving other controllable structure-guided image-to-image translation tasks, such as landmark-guided facial expression translation and keypoint-guided person image generation.

Includes members:
  Member "attribute guided sketch generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi channel attention selection gan with cascaded semantic guidance for cross view image translation")
  Member "multi channel attention selection gan with cascaded semantic guidance for cross view image translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "cycle in cycle generative adversarial networks for keypoint guided image generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "semantic image synthesis with spatially adaptive normalization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "dual generator generative adversarial networks for multi domain image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "cross view image synthesis using conditional gans":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "gesturegan for hand gesture to gesture translation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "sketchygan towards diverse and realistic sketch to image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep self supervised representation learning for free hand sketch")

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)