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: "relgan multi domain image to image translation via relative attributes"
  Abstract: Multi-domain image-to-image translation has gained increasing attention recently. Previous methods take an image and some target attributes as inputs and generate an output image with the desired attributes. However, such methods have two limitations. First, these methods assume binary-valued attributes and thus cannot yield satisfactory results for fine-grained control. Second, these methods require specifying the entire set of target attributes, even if most of the attributes would not be changed. To address these limitations, we propose RelGAN, a new method for multi-domain image-to-image translation. The key idea is to use relative attributes, which describes the desired change on selected attributes. Our method is capable of modifying images by changing particular attributes of interest in a continuous manner while preserving the other attributes. Experimental results demonstrate both the quantitative and qualitative effectiveness of our method on the tasks of facial attribute transfer and interpolation.

Connected through hyperedge HE-1 (induced by source paper "informative sample mining network for multi domain image to image translation"):
  with members:
    - "hardness aware deep metric learning"
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "hard aware point to set deep metric for person re identification"
    - "sampling matters in deep embedding learning"
    - "attgan facial attribute editing by only changing what you want"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep metric learning with angular loss"
    - "unsupervised image to image translation networks"

Connected through hyperedge HE-2 (induced by source paper "clsgan selective attribute editing based on classification adversarial network"):
  with members:
    - "attribute guided unpaired image to image translation with semi supervised learning"
    - "fully featured attribute transfer"
    - "image to image translation with multi path consistency regularization"
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "ugan untraceable gan for multi domain face translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "dna gan learning disentangled representations from multi attribute images"

Connected through hyperedge HE-3 (induced by source paper "edit exemplar domain aware image to image translation"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "towards instance level image to image translation"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "multimodal unsupervised image to image translation"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "stereoscopic neural style transfer"
    - "latent filter scaling for multimodal unsupervised image to image translation"
    - "conditional image to image translation"

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)