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: "video object segmentation with re identification"
  Abstract: Conventional video segmentation methods often rely on temporal continuity to propagate masks. Such an assumption suffers from issues like drifting and inability to handle large displacement. To overcome these issues, we formulate an effective mechanism to prevent the target from being lost via adaptive object re-identification. Specifically, our Video Object Segmentation with Re-identification (VS-ReID) model includes a mask propagation module and a ReID module. The former module produces an initial probability map by flow warping while the latter module retrieves missing instances by adaptive matching. With these two modules iteratively applied, our VS-ReID records a global mean (Region Jaccard and Boundary F measure) of 0.699, the best performance in 2017 DAVIS Challenge.

Connected through hyperedge HE-1 (induced by source paper "automatic video object segmentation via motion appearance stream fusion and instance aware segmentation"):
  with members:
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "maskrnn instance level video object segmentation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the 2017 davis challenge on video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "anchor diffusion for unsupervised video object segmentation"):
  with members:
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "deeply supervised salient object detection with short connections"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "video salient object detection via fully convolutional networks"
    - "fast online object tracking and segmentation a unifying approach"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "tukey inspired video object segmentation"

Connected through hyperedge HE-3 (induced by source paper "lip learning instance propagation for video object segmentation"):
  with members:
    - "group normalization"
    - "fast and accurate online video object segmentation via tracking parts"
    - "efficient video object segmentation via network modulation"
    - "maskrnn instance level video object segmentation"
    - "videomatch matching based video object segmentation"
    - "path aggregation network for instance segmentation"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "video object segmentation with joint re identification and attention aware mask propagation"

Connected through hyperedge HE-4 (induced by source paper "ovsnet towards one pass real time video object segmentation"):
  with members:
    - "path aggregation network for instance segmentation"
    - "premvos proposal generation refinement and merging for video object segmentation"
    - "video object segmentation with joint re identification and attention aware mask propagation"
    - "the 2017 davis challenge on video object segmentation"
    - "lucid data dreaming for video object segmentation"
    - "online adaptation of convolutional neural networks for video object segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"

Connected through hyperedge HE-5 (induced by source paper "mhp vos multiple hypotheses propagation 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"
    - "videomatch matching based video object segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "the 2018 davis challenge on video object segmentation"
    - "pixel level matching for video object segmentation using convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "an empirical study of propagation based methods for video object segmentation"):
  with members:
    - "discriminative online learning for fast video object segmentation"
    - "mhp vos multiple hypotheses propagation for video object segmentation"
    - "video object segmentation using space time memory networks"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "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"
    - "a generative appearance model for end to end video object segmentation"

Connected through hyperedge HE-7 (induced by source paper "u net based multi instance video object segmentation"):
  with members:
    - "video object segmentation with joint re identification and attention aware mask propagation"
    - "the 2017 davis challenge on video object segmentation"
    - "online adaptation of convolutional neural networks for video object segmentation"
    - "mask r cnn"
    - "learning to segment object candidates"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "deep spatio temporal random fields for efficient video segmentation"):
  with members:
    - "the 2017 davis challenge on video object segmentation"
    - "lucid data dreaming for video object segmentation"
    - "semantic video cnns through representation warping"
    - "online adaptation of convolutional neural networks for video object segmentation"
    - "fusionseg learning to combine motion and appearance for fully automatic segmention of generic objects in videos"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

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)