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: "generative face completion"
  Abstract: In this paper, we propose an effective face completion algorithm using a deep generative model. Different from well-studied background completion, the face completion task is more challenging as it often requires to generate semantically new pixels for the missing key components (e.g., eyes and mouths) that contain large appearance variations. Unlike existing nonparametric algorithms that search for patches to synthesize, our algorithm directly generates contents for missing regions based on a neural network. The model is trained with a combination of a reconstruction loss, two adversarial losses and a semantic parsing loss, which ensures pixel faithfulness and local-global contents consistency. With extensive experimental results, we demonstrate qualitatively and quantitatively that our model is able to deal with a large area of missing pixels in arbitrary shapes and generate realistic face completion results.

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 "watch your up convolution cnn based generative deep neural networks are failing to reproduce spectral distributions"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "stabilizing gans with octave convolutions"
    - "figr few shot image generation with reptile"
    - "semi few shot attribute translation"
    - "band limited training and inference for convolutional neural networks"
    - "unmasking deepfakes with simple features"
    - "a fourier perspective on model robustness in computer vision"
    - "attributing fake images to gans learning and analyzing gan fingerprints"

Connected through hyperedge HE-3 (induced by source paper "cascade ef gan progressive facial expression editing with local focuses"):
  with members:
    - "suppressing uncertainties for large scale facial expression recognition"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep facial expression recognition a survey"
    - "spectral normalization for generative adversarial networks"
    - "geometry contrastive gan for facial expression transfer"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "large scale gan training for high fidelity natural image synthesis"
    - "ganimation anatomically aware facial animation from a single image"

Connected through hyperedge HE-4 (induced by source paper "deep portrait image completion and extrapolation"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "everybody dance now"
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "a generative model of people in clothing"
    - "be your own prada fashion synthesis with structural coherence"

Connected through hyperedge HE-5 (induced by source paper "grounded and controllable image completion by incorporating lexical semantics"):
  with members:
    - "universal dependency parsing from scratch"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "foreground aware image inpainting"
    - "pluralistic image completion"
    - "unconstrained foreground object search"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "text adaptive generative adversarial networks manipulating images with natural language"
    - "mc gan multi conditional generative adversarial network for image synthesis"

Connected through hyperedge HE-6 (induced by source paper "domain embedded multi model generative adversarial networks for image based face inpainting"):
  with members:
    - "image inpainting with learnable bidirectional attention maps"
    - "deep fusion network for image completion"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "structureflow image inpainting via structure aware appearance flow"
    - "foreground aware image inpainting"
    - "pluralistic image completion"
    - "alphagan generative adversarial networks for natural image matting"
    - "semantic image inpainting through improved wasserstein generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "joint face completion and super resolution using multi scale feature relation learning"):
  with members:
    - "facial expression restoration based on improved graph convolutional networks"
    - "fcsr gan joint face completion and super resolution via multi task learning"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "geometry aware face completion and editing"
    - "graph neural networks a review of methods and applications"
    - "rain streak removal for single image via kernel guided cnn"
    - "learning a discriminative prior for blind image deblurring"
    - "cycle dehaze enhanced cyclegan for single image dehazing"

Connected through hyperedge HE-8 (induced by source paper "guidance and evaluation semantic aware image inpainting for mixed scenes"):
  with members:
    - "learning pyramid context encoder network for high quality image inpainting"
    - "structureflow image inpainting via structure aware appearance flow"
    - "foreground aware image inpainting"
    - "coherent semantic attention for image inpainting"
    - "pluralistic image completion"
    - "semantic image synthesis with spatially adaptive normalization"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"

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)