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 "image generation from sketch constraint using contextual gan"):
  Abstract: In this paper we investigate image generation guided by hand sketch. When the input sketch is badly drawn, the output of common image-to-image translation follows the input edges due to the hard condition imposed by the translation process. Instead, we propose to use sketch as weak constraint, where the output edges do not necessarily follow the input edges. We address this problem using a novel joint image completion approach, where the sketch provides the image context for completing, or generating the output image. We train a generated adversarial network, i.e, contextual GAN to learn the joint distribution of sketch and the corresponding image by using joint images. Our contextual GAN has several advantages. First, the simple joint image representation allows for simple and effective learning of joint distribution in the same image-sketch space, which avoids complicated issues in cross-domain learning. Second, while the output is related to its input overall, the generated features exhibit more freedom in appearance and do not strictly align with the input features as previous conditional GANs do. Third, from the joint image's point of view, image and sketch are of no difference, thus exactly the same deep joint image completion network can be used for image-to-sketch generation. Experiments evaluated on three different datasets show that our contextual GAN can generate more realistic images than state-of-the-art conditional GANs on challenging inputs and generalize well on common categories.

Includes members:
  Member "generalisation and sharing in triplet convnets for sketch based visual search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sketchformer transformer based representation for sketched structure")
  Member "conditional image generation with pixelcnn decoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic latent residual video prediction")
  Member "generative image modeling using style and structure adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tailorgan making user defined fashion designs")
  Member "semantic image inpainting with deep generative models":
    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 "context encoders feature learning by inpainting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning representations by predicting bags of visual words")
  Member "generative visual manipulation on the natural image manifold":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "sketch based 3d shape retrieval using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sketch less for more on the fly fine grained sketch based image retrieval")

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)