human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "deep plastic surgery robust and controllable image editing with human drawn sketches"):
  with members:
    - "an unpaired sketch to photo translation model"
    - "controllable artistic text style transfer via shape matching gan"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "interactive sketch fill multiclass sketch to image translation"
    - "faceshop deep sketch based face image editing"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "sketchygan towards diverse and realistic sketch to image synthesis"

Connected through hyperedge HE-2 (induced by source paper "one to one mapping for unpaired image to image translation"):
  with members:
    - "towards learning a self inverse network for bidirectional image to image translation"
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "towards instance level image to image translation"
    - "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "analyzing inverse problems with invertible neural networks"
    - "recycle gan unsupervised video retargeting"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-3 (induced by source paper "matchinggan matching based few shot image generation"):
  with members:
    - "figr few shot image generation with reptile"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "few shot unsupervised image to image translation"
    - "cgans with projection discriminator"
    - "on first order meta learning algorithms"
    - "spectral normalization for generative adversarial networks"
    - "meta transfer learning for few shot learning"
    - "an empirical study on evaluation metrics of generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "image generation from freehand scene sketches"):
  with members:
    - "a layer based sequential framework for scene generation with gans"
    - "singan learning a generative model from a single natural image"
    - "semantic image synthesis with spatially adaptive normalization"
    - "specifying object attributes and relations in interactive scene generation"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "large scale gan training for high fidelity natural image synthesis"
    - "image generation from scene graphs"

Connected through hyperedge HE-5 (induced by source paper "learning structure appearance joint embedding for indoor scene image synthesis"):
  with members:
    - "learning to reconstruct 3d manhattan wireframes from a single image"
    - "semantic image synthesis with spatially adaptive normalization"
    - "end to end wireframe parsing"
    - "learning to predict layout to image conditional convolutions for semantic image synthesis"
    - "learning attraction field representation for robust line segment detection"
    - "multimodal unsupervised image to image translation"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-6 (induced by source paper "cali sketch stroke calibration and completion for high quality face image generation from poorly drawn sketches"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "csgan cyclic synthesized generative adversarial networks for image to image transformation"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "multi graph transformer for free hand sketch recognition"):
  with members:
    - "semantically tied paired cycle consistency for zero shot sketch based image retrieval"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "learning deep transformer models for machine translation"
    - "livesketch query perturbations for guided sketch based visual search"
    - "transformer xl attentive language models beyond a fixed length context"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "bp transformer modelling long range context via binary partitioning"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-8 (induced by source paper "attribute guided sketch generation"):
  with members:
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "instagan instance aware image to image translation"
    - "r btn cross domain face composite and synthesis from limited facial patches"
    - "deformable gans for pose based human image generation"
    - "unsupervised creation of parameterized avatars"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "semi supervised learning with context conditional generative adversarial networks"

Question: Please tell me which class the center node 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)