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: "deep grabcut for object selection"
  Abstract: Most previous bounding-box-based segmentation methods assume the bounding box tightly covers the object of interest. However it is common that a rectangle input could be too large or too small. In this paper, we propose a novel segmentation approach that uses a rectangle as a soft constraint by transforming it into an Euclidean distance map. A convolutional encoder-decoder network is trained end-to-end by concatenating images with these distance maps as inputs and predicting the object masks as outputs. Our approach gets accurate segmentation results given sloppy rectangles while being general for both interactive segmentation and instance segmentation. We show our network extends to curve-based input without retraining. We further apply our network to instance-level semantic segmentation and resolve any overlap using a conditional random field. Experiments on benchmark datasets demonstrate the effectiveness of the proposed approaches.

Connected through hyperedge HE-1 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "interactive full image segmentation by considering all regions jointly"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to segment via cut and paste"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual affordance and function understanding a survey"

Connected through hyperedge HE-2 (induced by source paper "sideinfnet a deep neural network for semi automatic semantic segmentation with side information"):
  with members:
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "a unified model for near and remote sensing"
    - "convolutional networks with adaptive inference graphs"
    - "pose2instance harnessing keypoints for person instance segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "large scale interactive object segmentation with human annotators"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "weakly supervised instance segmentation using class peak response"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "iteratively trained interactive segmentation"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "revisiting unreasonable effectiveness of data in deep learning era"

Connected through hyperedge HE-4 (induced by source paper "fast user guided video object segmentation by interaction and propagation networks"):
  with members:
    - "interactive video object segmentation in the wild"
    - "efficient video object segmentation via network modulation"
    - "the 2018 davis challenge on video object segmentation"
    - "pixel level matching for video object segmentation using convolutional neural networks"
    - "the 2017 davis challenge on video object segmentation"
    - "video object segmentation without temporal information"
    - "real time user guided image colorization with learned deep priors"
    - "learning video object segmentation with visual memory"

Connected through hyperedge HE-5 (induced by source paper "youtube vos sequence to sequence video object segmentation"):
  with members:
    - "efficient video object segmentation via network modulation"
    - "maskrnn instance level video object segmentation"
    - "the 2017 davis challenge on video object segmentation"
    - "learning video object segmentation with visual memory"
    - "online adaptation of convolutional neural networks for video object segmentation"
    - "segflow joint learning for video object segmentation and optical flow"
    - "unsupervised learning for physical interaction through video prediction"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-6 (induced by source paper "a fully convolutional two stream fusion network for interactive image segmentation"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "iteratively trained interactive segmentation"
    - "deep interactive region segmentation and captioning"
    - "deep extreme cut from extreme points to object segmentation"
    - "fast image processing with fully convolutional networks"
    - "training region based object detectors with online hard example mining"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"

Connected through hyperedge HE-7 (induced by source paper "deep object co segmentation"):
  with members:
    - "deep contrast learning for salient object detection"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "pyramid scene parsing network"
    - "deep interactive object selection"
    - "u net convolutional networks for biomedical image segmentation"
    - "flownet learning optical flow with convolutional networks"
    - "conditional random fields as recurrent neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "premvos proposal generation refinement and merging for video object segmentation"):
  with members:
    - "fast and accurate online video object segmentation via tracking parts"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "youtube vos sequence to sequence video object segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "the 2018 davis challenge on video object segmentation"
    - "video object segmentation with joint re identification and attention aware mask propagation"
    - "video object segmentation with re identification"

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)