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: "generating a fusion image one s identity and another s shape"
  Abstract: Generating a novel image by manipulating two input images is an interesting research problem in the study of generative adversarial networks (GANs). We propose a new GAN-based network that generates a fusion image with the identity of input image x and the shape of input image y. Our network can simultaneously train on more than two image datasets in an unsupervised manner. We define an identity loss LI to catch the identity of image x and a shape loss LS to get the shape of y. In addition, we propose a novel training method called Min-Patch training to focus the generator on crucial parts of an image, rather than its entirety. We show qualitative results on the VGG Youtube Pose dataset, Eye dataset (MPIIGaze and UnityEyes), and the Photo-Sketch-Cartoon dataset.

Connected through hyperedge HE-1 (induced by source paper "a novel bilevel paradigm for image to image translation"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "few shot segmentation propagation with guided networks"
    - "every smile is unique landmark guided diverse smile generation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-2 (induced by source paper "learning disentangled representations via independent subspaces"):
  with members:
    - "structuring autoencoders"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "mask guided portrait editing with conditional gans"
    - "uncalibrated non rigid factorisation by independent subspace analysis"
    - "markov chain neural networks"
    - "attgan facial attribute editing by only changing what you want"
    - "deep learning for vanishing point detection using an inverse gnomonic projection"

Connected through hyperedge HE-3 (induced by source paper "video synthesis of human upper body with realistic face"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "recycle gan unsupervised video retargeting"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "everybody dance now"
    - "learning to generate long term future via hierarchical prediction"
    - "pose guided person image generation"
    - "dynamics transfer gan generating video by transferring arbitrary temporal dynamics from a source video to a single target image"
    - "cyclegan face off"

Connected through hyperedge HE-4 (induced by source paper "animating arbitrary objects via deep motion transfer"):
  with members:
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "3d guided fine grained face manipulation"
    - "video to video synthesis"
    - "synthesizing images of humans in unseen poses"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "recycle gan unsupervised video retargeting"
    - "unsupervised learning of object landmarks through conditional image generation"

Connected through hyperedge HE-5 (induced by source paper "everybody dance now"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "synthesizing images of humans in unseen poses"
    - "recycle gan unsupervised video retargeting"
    - "deep video based performance cloning"
    - "lookingood enhancing performance capture with real time neural re rendering"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "densepose dense human pose estimation in the wild"

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)