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 without temporal information"
  Abstract: Video Object Segmentation, and video processing in general, has been historically dominated by methods that rely on the temporal consistency and redundancy in consecutive video frames. When the temporal smoothness is suddenly broken, such as when an object is occluded, or some frames are missing in a sequence, the result of these methods can deteriorate significantly or they may not even produce any result at all. This paper explores the orthogonal approach of processing each frame independently, i.e disregarding the temporal information. In particular, it tackles the task of semi-supervised video object segmentation: the separation of an object from the background in a video, given its mask in the first frame. We present Semantic One-Shot Video Object Segmentation (OSVOS-S), based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test sequence (hence one shot). We show that instance level semantic information, when combined effectively, can dramatically improve the results of our previous method, OSVOS. We perform experiments on two recent video segmentation databases, which show that OSVOS-S is both the fastest and most accurate method in the state of the art.

Connected through hyperedge HE-1 (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-2 (induced by source paper "video object segmentation using space time memory networks"):
  with members:
    - "boltvos box level tracking for video object segmentation"
    - "rvos end to end recurrent network for video object segmentation"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "a memory network approach for story based temporal summarization of 360 deg videos"
    - "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"
    - "maskrnn instance level video object segmentation"

Connected through hyperedge HE-3 (induced by source paper "meta learning with differentiable closed form solver for fast video object segmentation"):
  with members:
    - "mask propagation network 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"
    - "meta learning with differentiable closed form solvers"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "prototypical networks for few shot learning"
    - "learning to remember rare events"
    - "online adaptation of convolutional neural networks for video object segmentation"

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"
    - "deep grabcut for object selection"
    - "pixel level matching for video object segmentation using convolutional neural networks"
    - "the 2017 davis challenge on video object segmentation"
    - "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 "in defense of osvos"):
  with members:
    - "learning pairwise relationship for multi object detection in crowded scenes"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "mask propagation network for video object segmentation"
    - "fast and accurate online video object segmentation via tracking parts"
    - "yolov3 an incremental improvement"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "fast online object tracking and segmentation a unifying approach"

Connected through hyperedge HE-6 (induced by source paper "spacetime graph optimization for video object segmentation"):
  with members:
    - "unsupervised online video object segmentation with motion property understanding"
    - "fast and accurate online video object segmentation via tracking parts"
    - "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"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "unsupervised object segmentation in video by efficient selection of highly probable positive features"
    - "unsupervised learning from video to detect foreground objects in single images"
    - "learning video object segmentation with visual memory"

Connected through hyperedge HE-7 (induced by source paper "adaptive roi generation for video object segmentation using reinforcement learning"):
  with members:
    - "fast and accurate online video object segmentation via tracking parts"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "correlation filter selection for visual tracking using reinforcement learning"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "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"

Connected through hyperedge HE-8 (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"

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)