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: "an unpaired sketch to photo translation model"
  Abstract: Sketch-based image synthesis aims to generate a photo image given a sketch. It is a challenging task; because sketches are drawn by non-professionals and only consist of strokes, they usually exhibit shape deformation and lack visual cues, i.e., colors and textures. Thus translation from sketch to photo involves two aspects: shape and color (texture). Existing methods cannot handle this task well, as they mostly focus on solving one translation. In this work, we show that the key to this task lies in decomposing the translation into two sub-tasks, shape translation and colorization. Correspondingly, we propose a model consisting of two sub-networks, with each one tackling one sub-task. We also find that, when translating shapes, specific drawing styles affect the generated results significantly and may even lead to failure. To make our model more robust to drawing style variations, we design a data augmentation strategy and re-purpose an attention module, aiming to make our model pay less attention to distracted regions of a sketch. Besides, a conditional module is adapted for color translation to improve diversity and increase users' control over the generated results. Both quantitative and qualitative comparisons are presented to show the superiority of our approach. In addition, as a side benefit, our model can synthesize high-quality sketches from photos inversely. We also demonstrate how these generated photos and sketches can benefit other applications, such as sketch-based image retrieval.

Connected through hyperedge HE-1 (induced by source paper "deep plastic surgery robust and controllable image editing with human drawn sketches"):
  with members:
    - "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"
    - "free form image inpainting with gated convolution"

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)