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: "interactive sketch fill multiclass sketch to image translation"
  Abstract: We propose an interactive GAN-based sketch-to-image translation method that helps novice users create images of simple objects. As the user starts to draw a sketch of a desired object type, the network interactively recommends plausible completions, and shows a corresponding synthesized image to the user. This enables a feedback loop, where the user can edit their sketch based on the network's recommendations, visualizing both the completed shape and final rendered image while they draw. In order to use a single trained model across a wide array of object classes, we introduce a gating-based approach for class conditioning, which allows us to generate distinct classes without feature mixing, from a single generator network. Video available at our website: this https URL.

Connected through hyperedge HE-1 (induced by source paper "sketch less for more on the fly fine grained sketch based image retrieval"):
  with members:
    - "semantically tied paired cycle consistency for zero shot sketch based image retrieval"
    - "semantic aware knowledge preservation for zero shot sketch based image retrieval"
    - "livesketch query perturbations for guided sketch based visual search"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "sodeep a sorting deep net to learn ranking loss surrogates"
    - "pluralistic image completion"
    - "goal driven sequential data abstraction"
    - "learning deep sketch abstraction"

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"
    - "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"
    - "free form image inpainting with gated convolution"

Connected through hyperedge HE-3 (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"
    - "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"
    - "sketchygan towards diverse and realistic sketch to image synthesis"

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)