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: "on face segmentation face swapping and face perception"
  Abstract: We show that even when face images are unconstrained and arbitrarily paired, face swapping between them is actually quite simple. To this end, we make the following contributions. (a) Instead of tailoring systems for face segmentation, as others previously proposed, we show that a standard fully convolutional network (FCN) can achieve remarkably fast and accurate segmentations, provided that it is trained on a rich enough example set. For this purpose, we describe novel data collection and generation routines which provide challenging segmented face examples. (b) We use our segmentations to enable robust face swapping under unprecedented conditions. (c) Unlike previous work, our swapping is robust enough to allow for extensive quantitative tests. To this end, we use the Labeled Faces in the Wild (LFW) benchmark and measure the effect of intra- and inter-subject face swapping on recognition. We show that our intra-subject swapped faces remain as recognizable as their sources, testifying to the effectiveness of our method. In line with well known perceptual studies, we show that better face swapping produces less recognizable inter-subject results. This is the first time this effect was quantitatively demonstrated for machine vision systems.

Connected through hyperedge HE-1 (induced by source paper "live face de identification in video"):
  with members:
    - "feature denoising for improving adversarial robustness"
    - "privacy protective gan for face de identification"
    - "arcface additive angular margin loss for deep face recognition"
    - "multimodal unsupervised image to image translation"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "a hybrid model for identity obfuscation by face replacement"
    - "drit diverse image to image translation via disentangled representations"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-2 (induced by source paper "fast face image synthesis with minimal training"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "recent progress of face image synthesis"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "face aging with conditional generative adversarial networks"
    - "magan margin adaptation for generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "rsgan face swapping and editing using face and hair representation in latent spaces"):
  with members:
    - "global and local consistent age generative adversarial networks"
    - "high resolution face completion with multiple controllable attributes via fully end to end progressive generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "lr gan layered recursive generative adversarial networks for image generation"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "generating images part by part with composite generative adversarial networks"
    - "neural photo editing with introspective adversarial networks"
    - "pyramid scene parsing network"

Connected through hyperedge HE-4 (induced by source paper "extreme 3d face reconstruction looking past occlusions"):
  with members:
    - "expnet landmark free deep 3d facial expressions"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "end to end 3d face reconstruction with deep neural networks"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "sfsnet learning shape reflectance and illuminance of faces in the wild"
    - "self supervised multi level face model learning for monocular reconstruction at over 250 hz"
    - "do we really need to collect millions of faces for effective face recognition"

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)