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: "a detailed rubric for motion segmentation"
  Abstract: Motion segmentation is currently an active area of research in computer Vision. The task of comparing different methods of motion segmentation is complicated by the fact that researchers may use subtly different definitions of the problem. Questions such as "Which objects are moving?", "What is background?", and "How can we use motion of the camera to segment objects, whether they are static or moving?" are clearly related to each other, but lead to different algorithms, and imply different versions of the ground truth. This report has two goals. The first is to offer a precise definition of motion segmentation so that the intent of an algorithm is as well-defined as possible. The second is to report on new versions of three previously existing data sets that are compatible with this definition. We hope that this more detailed definition, and the three data sets that go with it, will allow more meaningful comparisons of certain motion segmentation methods.

Connected through hyperedge HE-1 (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-2 (induced by source paper "the 2019 davis challenge on vos unsupervised multi object segmentation"):
  with members:
    - "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"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "deep spatio temporal random fields for efficient video segmentation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"

Connected through hyperedge HE-3 (induced by source paper "towards segmenting everything that moves"):
  with members:
    - "a structured model for action detection"
    - "object discovery in videos as foreground motion clustering"
    - "videos as space time region graphs"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "youtube vos a large scale video object segmentation benchmark"
    - "learning to segment moving objects"
    - "the 2017 davis challenge on video object segmentation"
    - "learning to segment every thing"

Connected through hyperedge HE-4 (induced by source paper "3d rigid motion segmentation with mixed and unknown number of models"):
  with members:
    - "motion segmentation by exploiting complementary geometric models"
    - "higher order minimum cost lifted multicuts for motion segmentation"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-5 (induced by source paper "object discovery in videos as foreground motion clustering"):
  with members:
    - "recurrent pixel embedding for instance grouping"
    - "the 2017 davis challenge on video object segmentation"
    - "interactive perception leveraging action in perception and perception in action"
    - "learning video object segmentation with visual memory"
    - "fusionseg learning to combine motion and appearance for fully automatic segmention of generic objects in videos"
    - "da rnn semantic mapping with data associated recurrent neural networks"
    - "semantic instance segmentation via deep metric learning"
    - "semantic instance segmentation with a discriminative loss function"

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)