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: "spg net segmentation prediction and guidance network for image inpainting"
  Abstract: In this paper, we focus on image inpainting task, aiming at recovering the missing area of an incomplete image given the context information. Recent development in deep generative models enables an efficient end-to-end framework for image synthesis and inpainting tasks, but existing methods based on generative models don't exploit the segmentation information to constrain the object shapes, which usually lead to blurry results on the boundary. To tackle this problem, we propose to introduce the semantic segmentation information, which disentangles the inter-class difference and intra-class variation for image inpainting. This leads to much clearer recovered boundary between semantically different regions and better texture within semantically consistent segments. Our model factorizes the image inpainting process into segmentation prediction (SP-Net) and segmentation guidance (SG-Net) as two steps, which predict the segmentation labels in the missing area first, and then generate segmentation guided inpainting results. Experiments on multiple public datasets show that our approach outperforms existing methods in optimizing the image inpainting quality, and the interactive segmentation guidance provides possibilities for multi-modal predictions of image inpainting.

Connected through hyperedge HE-1 (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-2 (induced by source paper "spgnet semantic prediction guidance for scene parsing"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "seamless scene segmentation"
    - "deeperlab single shot image parser"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "multi scale structure aware network for human pose estimation"

Connected through hyperedge HE-3 (induced by source paper "harmonizing maximum likelihood with gans for multimodal conditional generation"):
  with members:
    - "on the convergence of adam and beyond"
    - "video to video synthesis"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "video prediction with appearance and motion conditions"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "image inpainting using block wise procedural training with annealed adversarial counterpart"
    - "free form image inpainting with gated convolution"

Connected through hyperedge HE-4 (induced by source paper "pi rec progressive image reconstruction network with edge and color domain"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "smit stochastic multi label image to image translation"
    - "probabilistic semantic inpainting with pixel constrained cnns"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "deep fusion network for image completion"):
  with members:
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "structural inpainting"
    - "free form image inpainting with gated convolution"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "image inpainting via generative multi column convolutional neural networks"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-6 (induced by source paper "pluralistic image completion"):
  with members:
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"
    - "high resolution face completion with multiple controllable attributes via fully end to end progressive generative adversarial networks"
    - "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-7 (induced by source paper "one stage inpainting with bilateral attention and pyramid filling block"):
  with members:
    - "image inpainting with learnable bidirectional attention maps"
    - "foreground aware image inpainting"
    - "coherent semantic attention for image inpainting"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "the relativistic discriminator a key element missing from standard gan"
    - "free form image inpainting with gated convolution"
    - "shift net image inpainting via deep feature rearrangement"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-8 (induced by source paper "aads augmented autonomous driving simulation using data driven algorithms"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "augmented lidar simulator for autonomous driving"
    - "heter sim heterogeneous multi agent systems simulation by interactive data driven optimization"
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "trafficpredict trajectory prediction for heterogeneous traffic agents"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "autonovi autonomous vehicle planning with dynamic maneuvers and traffic constraints"
    - "carla an open urban driving simulator"

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)