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: "sketchygan towards diverse and realistic sketch to image synthesis"
  Abstract: Synthesizing realistic images from human drawn sketches is a challenging problem in computer graphics and vision. Existing approaches either need exact edge maps, or require a database to retrieve images from. In this work, we propose a novel Generative Adversarial Network (GAN) approach that synthesizes realistic looking images from 50 categories including motorcycles, horses and couches. We demonstrate a data augmentation technique for sketches which is fully automatic, and we show that the augmented data is helpful to our task. We introduce a new building block suit for both the generator and discriminator which improves the information flow and utilizes input images at multiple scales. Compared to state-of-the-art image translation methods, our approach generates more realistic images and achieves significantly higher Inception Scores.

Connected through hyperedge HE-1 (induced by source paper "deep self supervised representation learning for free hand sketch"):
  with members:
    - "deep learning for free hand sketch a survey"
    - "zero reference deep curve estimation for low light image enhancement"
    - "generative adversarial zero shot relational learning for knowledge graphs"
    - "the devil is in the channels mutual channel loss for fine grained image classification"
    - "multi graph transformer for free hand sketch recognition"
    - "learning for multi model and multi type fitting"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-2 (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"
    - "free form image inpainting with gated convolution"

Connected through hyperedge HE-3 (induced by source paper "sketch to art synthesizing stylized art images from sketches"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "a style based generator architecture for generative adversarial networks"
    - "the shape of art history in the eyes of the machine"
    - "style and content disentanglement in generative adversarial networks"
    - "drit diverse image to image translation via disentangled representations"

Connected through hyperedge HE-4 (induced by source paper "synthesizing human like sketches from natural images using a conditional convolutional decoder"):
  with members:
    - "learning deep sketch abstraction"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "unsupervised learning of depth and ego motion from video"
    - "a neural representation of sketch drawings"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-5 (induced by source paper "ganilla generative adversarial networks for image to illustration translation"):
  with members:
    - "travelgan image to image translation by transformation vector learning"
    - "few shot unsupervised image to image translation"
    - "interactive sketch fill multiclass sketch to image translation"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "image to image translation via group wise deep whitening and coloring transformation"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"
    - "latent filter scaling for multimodal unsupervised image to image translation"

Connected through hyperedge HE-6 (induced by source paper "affect based intrinsic rewards for exploration and learning"):
  with members:
    - "monocular plan view networks for autonomous driving"
    - "explorations and lessons learned in building an autonomous formula sae car from simulations"
    - "learning exploration policies for navigation"
    - "large scale study of curiosity driven learning"
    - "visceral machines risk aversion in reinforcement learning with intrinsic physiological rewards"
    - "on learning intrinsic rewards for policy gradient methods"
    - "episodic curiosity through reachability"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"

Connected through hyperedge HE-7 (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"
    - "large scale gan training for high fidelity natural image synthesis"
    - "image generation from scene graphs"
    - "image generation from layout"

Connected through hyperedge HE-8 (induced by source paper "type i attack for generative models"):
  with members:
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "colorfool semantic adversarial colorization"
    - "adversarial attacks on variational autoencoders"
    - "semantic adversarial examples"
    - "arcface additive angular margin loss for deep face recognition"
    - "adversarial attack type i cheat classifiers by significant changes"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

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)