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: "shift net image inpainting via deep feature rearrangement"
  Abstract: Deep convolutional networks (CNNs) have exhibited their potential in image inpainting for producing plausible results.However, in most existing methods, e.g., context encoder, the missing parts are predicted by propagating the surrounding convolutional features through a fully connected layer, which intends to produce semantically plausible but blurry result. In this paper, we introduce a special shift-connection layer to the U-Net architecture, namely Shift-Net, for filling in missing regions of any shape with sharp structures and fine-detailed textures. To this end, the encoder feature of the known region is shifted to serve as an estimation of the missing parts. A guidance loss is introduced on decoder feature to minimize the distance between the decoder feature after fully connected layer and the ground truth encoder feature of the missing parts. With such constraint, the decoder feature in missing region can be used to guide the shift of encoder feature in known region. An end-to-end learning algorithm is further developed to train the Shift-Net. Experiments on the Paris StreetView and Places datasets demonstrate the efficiency and effectiveness of our Shift-Net in producing sharper, fine-detailed, and visually plausible results.

Connected through hyperedge HE-1 (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-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"
    - "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-3 (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-4 (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"

Connected through hyperedge HE-5 (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"
    - "high resolution face completion with multiple controllable attributes via fully end to end progressive generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "image inpainting via generative multi column convolutional neural networks"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-6 (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"
    - "pluralistic image completion"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "symmetric skip connection wasserstein gan for high resolution facial image inpainting"):
  with members:
    - "image inpainting for irregular holes using partial convolutions"
    - "semi parametric image inpainting"
    - "generative image inpainting with contextual attention"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep image prior"
    - "rethinking atrous convolution for semantic image segmentation"
    - "perceptual losses for real time style transfer and super resolution"
    - "high resolution image inpainting using multi scale neural patch synthesis"

Connected through hyperedge HE-8 (induced by source paper "180 degree outpainting from a single image"):
  with members:
    - "foreground aware image inpainting"
    - "free form image inpainting with gated convolution"
    - "painting outside the box image outpainting with gans"
    - "generative image inpainting with contextual attention"
    - "sideeye a generative neural network based simulator of human peripheral vision"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "least squares generative adversarial networks"
    - "context encoders feature learning by inpainting"

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)