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: "high resolution face completion with multiple controllable attributes via fully end to end progressive generative adversarial networks"
  Abstract: We present a deep learning approach for high resolution face completion with multiple controllable attributes (e.g., male and smiling) under arbitrary masks. Face completion entails understanding both structural meaningfulness and appearance consistency locally and globally to fill in "holes" whose content do not appear elsewhere in an input image. It is a challenging task with the difficulty level increasing significantly with respect to high resolution, the complexity of "holes" and the controllable attributes of filled-in fragments. Our system addresses the challenges by learning a fully end-to-end framework that trains generative adversarial networks (GANs) progressively from low resolution to high resolution with conditional vectors encoding controllable attributes. #R##N#We design novel network architectures to exploit information across multiple scales effectively and efficiently. We introduce new loss functions encouraging sharp completion. We show that our system can complete faces with large structural and appearance variations using a single feed-forward pass of computation with mean inference time of 0.007 seconds for images at 1024 x 1024 resolution. We also perform a pilot human study that shows our approach outperforms state-of-the-art face completion methods in terms of rank analysis. The code will be released upon publication.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy"):
  with members:
    - "a layer based sequential framework for scene generation with gans"
    - "how generative adversarial networks and their variants work an overview"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "quick and easy time series generation with established image based gans"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "the gan landscape losses architectures regularization and normalization"
    - "deep semantic hashing with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "piigan generative adversarial networks for pluralistic image inpainting"):
  with members:
    - "learning pyramid context encoder network for high quality image inpainting"
    - "coherent semantic attention for image inpainting"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "shift net image inpainting via deep feature rearrangement"
    - "image inpainting via generative multi column convolutional neural networks"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-3 (induced by source paper "instance separation emerges from inpainting"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "learning correspondence from the cycle consistency of time"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "selfie self supervised pretraining for image embedding"
    - "structureflow image inpainting via structure aware appearance flow"
    - "unsupervised object segmentation by redrawing"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "noise2void learning denoising from single noisy images"

Connected through hyperedge HE-4 (induced by source paper "make a face towards arbitrary high fidelity face manipulation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "on learning 3d face morphable model from in the wild images"
    - "pose guided human video generation"
    - "look at boundary a boundary aware face alignment algorithm"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "hallucinated iqa no reference image quality assessment via adversarial learning"
    - "self supervised learning of a facial attribute embedding from video"
    - "blind predicting similar quality map for image quality assessment"

Connected through hyperedge HE-5 (induced by source paper "pluralistic image completion"):
  with members:
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"
    - "free form image inpainting with gated convolution"
    - "shift net image inpainting via deep feature rearrangement"
    - "generative image inpainting with contextual attention"
    - "drit diverse image to image translation via disentangled representations"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-6 (induced by source paper "pose variant 3d facial attribute generation"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "arcface additive angular margin loss for deep face recognition"
    - "towards open set identity preserving face synthesis"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "ganimation anatomically aware facial animation from a single image"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "dna gan learning disentangled representations from multi attribute images"

Connected through hyperedge HE-7 (induced by source paper "does generative face completion help face recognition"):
  with members:
    - "geometry aware face completion and editing"
    - "identity preserving face completion for large ocular region occlusion"
    - "free form image inpainting with gated convolution"
    - "additive margin softmax for face verification"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "segmentation aware convolutional networks using local attention masks"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-8 (induced by source paper "diversity generated image inpainting with style extraction"):
  with members:
    - "learning pyramid context encoder network for high quality image inpainting"
    - "coherent semantic attention for image inpainting"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"
    - "shift net image inpainting via deep feature rearrangement"
    - "image inpainting via generative multi column convolutional neural networks"
    - "generative image inpainting with contextual attention"

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)