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: "semantically guided video object segmentation"
  Abstract: This paper tackles the problem of semi-supervised video object segmentation, that is, segmenting an object in a sequence given its mask in the first frame. One of the main challenges in this scenario is the change of appearance of the objects of interest. Their semantics, on the other hand, do not vary. This paper investigates how to take advantage of such invariance via the introduction of a semantic prior that guides the appearance model. Specifically, given the segmentation mask of the first frame of a sequence, we estimate the semantics of the object of interest, and propagate that knowledge throughout the sequence to improve the results based on an appearance model. We present Semantically-Guided Video Object Segmentation (SGV), which improves results over previous state of the art on two different datasets using a variety of evaluation metrics, while running in half a second per frame.

Connected through hyperedge HE-1 (induced by source paper "videomatch matching based video object segmentation"):
  with members:
    - "fast and accurate online video object segmentation via tracking parts"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "efficient video object segmentation via network modulation"
    - "maskrnn instance level video object segmentation"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "video object segmentation with re identification"
    - "convolutional neural network architecture for geometric matching"
    - "pixel level matching for video object segmentation using convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "blazingly fast video object segmentation with pixel wise metric learning"):
  with members:
    - "efficient video object segmentation via network modulation"
    - "road reality oriented adaptation for semantic segmentation of urban scenes"
    - "the 2017 davis challenge on video object segmentation"
    - "video object segmentation without temporal information"
    - "one shot learning for semantic 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"

Connected through hyperedge HE-3 (induced by source paper "maskrnn instance level video object segmentation"):
  with members:
    - "the 2017 davis challenge on video object segmentation"
    - "lucid data dreaming for video object segmentation"
    - "mask r cnn"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "video propagation networks"
    - "fast r cnn"
    - "fully connected deep structured networks"
    - "adam a method for stochastic optimization"

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)