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: "instance embedding transfer to unsupervised video object segmentation"
  Abstract: We propose a method for unsupervised video object segmentation by transferring the knowledge encapsulated in image-based instance embedding networks. The instance embedding network produces an embedding vector for each pixel that enables identifying all pixels belonging to the same object. Though trained on static images, the instance embeddings are stable over consecutive video frames, which allows us to link objects together over time. Thus, we adapt the instance networks trained on static images to video object segmentation and incorporate the embeddings with objectness and optical flow features, without model retraining or online fine-tuning. The proposed method outperforms state-of-the-art unsupervised segmentation methods in the DAVIS dataset and the FBMS dataset.

Connected through hyperedge HE-1 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos"):
  with members:
    - "zero shot video object segmentation via attentive graph neural networks"
    - "key instance selection for unsupervised video object segmentation"
    - "large scale object mining for object discovery from unlabeled video"
    - "an efficient 3d cnn for action object segmentation in video"
    - "video instance segmentation"
    - "video object segmentation using space time memory networks"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "dmm net differentiable mask matching network for video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "see more know more unsupervised video object segmentation with co attention siamese networks"):
  with members:
    - "deep visual attention prediction"
    - "fast and accurate online video object segmentation via tracking parts"
    - "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"
    - "efficient video object segmentation via network modulation"
    - "videomatch matching based video object segmentation"
    - "pairwise body part attention for recognizing human object interactions"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "zero shot video object segmentation via attentive graph neural networks"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "reasoning visual dialogs with structural and partial observations"
    - "rvos end to end recurrent network for video object segmentation"
    - "ranet ranking attention network for fast video object segmentation"
    - "semantic aware attention based deep object co segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "learning human object interactions by graph parsing neural networks"
    - "videos as space time region graphs"

Connected through hyperedge HE-4 (induced by source paper "unovost unsupervised offline video object segmentation and tracking"):
  with members:
    - "key instance selection for unsupervised video object segmentation"
    - "large scale object mining for object discovery from unlabeled video"
    - "video instance segmentation"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "towards segmenting everything that moves"
    - "rvos end to end recurrent network for video object segmentation"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "fast and accurate online video object segmentation via tracking parts"

Connected through hyperedge HE-5 (induced by source paper "motion attentive transition for zero shot video object segmentation"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "zero shot video object segmentation via attentive graph neural networks"
    - "rvos end to end recurrent network for video object segmentation"
    - "salient objects in clutter bringing salient object detection to the foreground"
    - "video object segmentation using teacher student adaptation in a human robot interaction hri setting"
    - "cbam convolutional block attention module"
    - "learning to segment moving objects"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-6 (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"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep grabcut for object selection"
    - "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-7 (induced by source paper "video object segmentation and tracking a survey"):
  with members:
    - "interactive video object segmentation in the wild"
    - "fast and accurate online video object segmentation via tracking parts"
    - "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"
    - "videomatch matching based video object segmentation"
    - "unsupervised video object segmentation for deep reinforcement learning"
    - "youtube vos sequence to sequence video object segmentation"

Connected through hyperedge HE-8 (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"
    - "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"
    - "video object segmentation with re identification"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the 2017 davis challenge on video object segmentation"

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)