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: "pluralistic image completion"
  Abstract: Most image completion methods produce only one result for each masked input, although there may be many reasonable possibilities. In this paper, we present an approach for \textbf{pluralistic image completion} -- the task of generating multiple and diverse plausible solutions for image completion. A major challenge faced by learning-based approaches is that usually only one ground truth training instance per label. As such, sampling from conditional VAEs still leads to minimal diversity. To overcome this, we propose a novel and probabilistically principled framework with two parallel paths. One is a reconstructive path that utilizes the only one given ground truth to get prior distribution of missing parts and rebuild the original image from this distribution. The other is a generative path for which the conditional prior is coupled to the distribution obtained in the reconstructive path. Both are supported by GANs. We also introduce a new short+long term attention layer that exploits distant relations among decoder and encoder features, improving appearance consistency. When tested on datasets with buildings (Paris), faces (CelebA-HQ), and natural images (ImageNet), our method not only generated higher-quality completion results, but also with multiple and diverse plausible outputs.

Connected through hyperedge HE-1 (induced by source paper "sketch less for more on the fly fine grained sketch based image retrieval"):
  with members:
    - "semantically tied paired cycle consistency for zero shot sketch based image retrieval"
    - "semantic aware knowledge preservation for zero shot sketch based image retrieval"
    - "livesketch query perturbations for guided sketch based visual search"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "interactive sketch fill multiclass sketch to image translation"
    - "sodeep a sorting deep net to learn ranking loss surrogates"
    - "goal driven sequential data abstraction"
    - "learning deep sketch abstraction"

Connected through hyperedge HE-2 (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"
    - "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"
    - "hole filling with multiple reference views in dibr view synthesis"

Connected through hyperedge HE-3 (induced by source paper "discriminator feature based inference by recycling the discriminator of gans"):
  with members:
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "improved training of generative adversarial networks using representative features"
    - "group normalization"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "which training methods for gans do actually converge"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-4 (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"
    - "alphagan generative adversarial networks for natural image matting"
    - "semantic image inpainting through improved wasserstein generative adversarial networks"
    - "face completion with semantic knowledge and collaborative adversarial learning"

Connected through hyperedge HE-5 (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"
    - "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"
    - "spg net segmentation prediction and guidance network for image inpainting"

Connected through hyperedge HE-6 (induced by source paper "vcnet a robust approach to blind image inpainting"):
  with members:
    - "image inpainting with learnable bidirectional attention maps"
    - "foreground aware image inpainting"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "spectral normalization for generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "a style based generator architecture for generative adversarial networks"
    - "the contextual loss for image transformation with non aligned data"
    - "image inpainting via generative multi column convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "image fine grained inpainting"):
  with members:
    - "progressive perception oriented network for single image super resolution"
    - "structureflow image inpainting via structure aware appearance flow"
    - "foreground aware image inpainting"
    - "non stationary texture synthesis by adversarial expansion"
    - "the relativistic discriminator a key element missing from standard gan"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "spherical image generation from a single normal field of view image by considering scene symmetry"):
  with members:
    - "360 panorama synthesis from a sparse set of images with unknown fov"
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "spherephd applying cnns on a spherical polyhedron representation of 360 degree images"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "generative face completion"
    - "learning spherical convolution for fast features from 360 deg imagery"
    - "learning so 3 equivariant representations with spherical cnns"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

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)