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: "super trajectory for video segmentation"
  Abstract: We introduce a novel semi-supervised video segmentation approach based on an efficient video representation, called as "super-trajectory". Each super-trajectory corresponds to a group of compact trajectories that exhibit consistent motion patterns, similar appearance and close spatiotemporal relationships. We generate trajectories using a probabilistic model, which handles occlusions and drifts in a robust and natural way. To reliably group trajectories, we adopt a modified version of the density peaks based clustering algorithm that allows capturing rich spatiotemporal relations among trajectories in the clustering process. The presented video representation is discriminative enough to accurately propagate the initial annotations in the first frame onto the remaining video frames. Extensive experimental analysis on challenging benchmarks demonstrate our method is capable of distinguishing the target objects from complex backgrounds and even reidentifying them after long-term occlusions.

Connected through hyperedge HE-1 (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"
    - "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"
    - "efficient video object segmentation via network modulation"
    - "videomatch matching based video object segmentation"
    - "pairwise body part attention for recognizing human object interactions"

Connected through hyperedge HE-2 (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"
    - "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"
    - "videomatch matching based video object segmentation"
    - "unsupervised video object segmentation for deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "main multi attention instance network for video segmentation"):
  with members:
    - "fast and accurate online video object segmentation via tracking parts"
    - "distractor aware siamese networks for visual object tracking"
    - "efficient video object segmentation via network modulation"
    - "maskrnn instance level video object segmentation"
    - "videomatch matching based video object segmentation"
    - "fast online object tracking and segmentation a unifying approach"
    - "youtube vos a large scale video object segmentation benchmark"
    - "video object segmentation with joint re identification and attention aware mask propagation"

Connected through hyperedge HE-4 (induced by source paper "exploiting geometric constraints on dense trajectories for motion saliency"):
  with members:
    - "revisiting video saliency a large scale benchmark and a new model"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "videomatch matching based video object segmentation"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "video object segmentation using teacher student adaptation in a human robot interaction hri setting"
    - "the 2018 davis challenge on video object segmentation"
    - "learning to segment moving objects"

Connected through hyperedge HE-5 (induced by source paper "video object segmentation with language referring expressions"):
  with members:
    - "interactive video object segmentation in the wild"
    - "object referring in videos with language and human gaze"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "maskrnn instance level video object segmentation"
    - "mattnet modular attention network for referring expression comprehension"
    - "actor and action video segmentation from a sentence"
    - "the 2018 davis challenge on video object segmentation"
    - "discriminative bimodal networks for visual localization and detection with natural language queries"

Connected through hyperedge HE-6 (induced by source paper "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"):
  with members:
    - "maskrnn instance level video object segmentation"
    - "pixel level matching for video object segmentation using convolutional neural networks"
    - "the 2017 davis challenge on video object segmentation"
    - "online adaptation of convolutional neural networks for video object segmentation"
    - "segflow joint learning for video object segmentation and optical flow"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "deep learning markov random field for semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-7 (induced by source paper "lucid data dreaming for video object segmentation"):
  with members:
    - "deep spatio temporal random fields for efficient video segmentation"
    - "premvos proposal generation refinement and merging for video object segmentation"
    - "video object segmentation with re identification"
    - "the 2017 davis challenge on video object segmentation"
    - "guided optical flow learning"
    - "learning video object segmentation with visual memory"
    - "learning from synthetic humans"
    - "online adaptation of convolutional neural networks for video object segmentation"

Connected through hyperedge HE-8 (induced by source paper "online adaptation of convolutional neural networks for video object segmentation"):
  with members:
    - "a deep convolutional neural network for background subtraction"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "bridging category level and instance level semantic image segmentation"
    - "learning motion patterns in videos"
    - "video propagation networks"
    - "wider or deeper revisiting the resnet model for visual recognition"
    - "learning to track at 100 fps with deep regression networks"
    - "deeptrack learning discriminative feature representations online for robust visual tracking"

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)