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: "unsupervised image to image translation with generative adversarial networks"
  Abstract: It's useful to automatically transform an image from its original form to some synthetic form (style, partial contents, etc.), while keeping the original structure or semantics. We define this requirement as the "image-to-image translation" problem, and propose a general approach to achieve it, based on deep convolutional and conditional generative adversarial networks (GANs), which has gained a phenomenal success to learn mapping images from noise input since 2014. In this work, we develop a two step (unsupervised) learning method to translate images between different domains by using unlabeled images without specifying any correspondence between them, so that to avoid the cost of acquiring labeled data. Compared with prior works, we demonstrated the capacity of generality in our model, by which variance of translations can be conduct by a single type of model. Such capability is desirable in applications like bidirectional translation

Connected through hyperedge HE-1 (induced by source paper "cor gan correlation capturing convolutional neural networks for generating synthetic healthcare records"):
  with members:
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "adversarially regularized autoencoders"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "mocogan decomposing motion and content for video generation"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-2 (induced by source paper "logan unpaired shape transform in latent overcomplete space"):
  with members:
    - "emerging disentanglement in auto encoder based unsupervised image content transfer"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "p2p net bidirectional point displacement net for shape transform"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "point convolutional neural networks by extension operators"
    - "multimodal unsupervised image to image translation"
    - "nam non adversarial unsupervised domain mapping"
    - "pcpnet learning local shape properties from raw point clouds"

Connected through hyperedge HE-3 (induced by source paper "characterizing bias in classifiers using generative models"):
  with members:
    - "predictive inequity in object detection"
    - "identifying bias in ai using simulation"
    - "progressive growing of gans for improved quality stability and variation"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "learning face age progression a pyramid architecture of gans"
    - "lr gan layered recursive generative adversarial networks for image generation"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "crossnet latent cross consistency for unpaired image translation"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "specular to diffuse translation for multi view reconstruction"
    - "nam non adversarial unsupervised domain mapping"
    - "unsupervised attention guided image to image translation"
    - "drit diverse image to image translation via disentangled representations"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "high diversity attribute guided face generation with gans"):
  with members:
    - "mcgan mean and covariance feature matching gan"
    - "began boundary equilibrium generative adversarial networks"
    - "it takes only two adversarial generator encoder networks"
    - "recent progress of face image synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "focal loss for dense object detection"
    - "controllable generative adversarial network"
    - "face aging with conditional generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "sparsely grouped multi task generative adversarial networks for facial attribute manipulation"):
  with members:
    - "face aging with contextual generative adversarial nets"
    - "to create what you tell generating videos from captions"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "triangle generative adversarial networks"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "progressive growing of gans for improved quality stability and variation"
    - "good semi supervised learning that requires a bad gan"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "handwriting recognition in low resource scripts using adversarial learning"):
  with members:
    - "confidence prediction for lexicon free ocr"
    - "vital visual tracking via adversarial learning"
    - "cross language framework for word recognition and spotting of indic scripts"
    - "synthetic data generation for indic handwritten text recognition"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "adversarial discriminative domain adaptation"
    - "attribute cnns for word spotting in handwritten documents"
    - "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections"

Connected through hyperedge HE-8 (induced by source paper "quality aware multimodal saliency detection via deep reinforcement learning"):
  with members:
    - "salient object detection a survey"
    - "collaborative deep reinforcement learning for joint object search"
    - "attention aware face hallucination via deep reinforcement learning"
    - "a unified rgb t saliency detection benchmark dataset baselines analysis and a novel approach"
    - "perceptual generative adversarial networks for small object detection"
    - "improved training of wasserstein gans"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"

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)