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: "higher order minimum cost lifted multicuts for motion segmentation"
  Abstract: Most state-of-the-art motion segmentation algorithms draw their potential from modeling motion differences of local entities such as point trajectories in terms of pairwise potentials in graphical models. Inference in instances of minimum cost multicut problems defined on such graphs al- lows to optimize the number of the resulting segments along with the segment assignment. However, pairwise potentials limit the discriminative power of the employed motion models to translational differences. More complex models such as Euclidean or affine transformations call for higher-order potentials and a tractable inference in the resulting higher-order graphical models. In this paper, we (1) introduce a generalization of the minimum cost lifted multicut problem to hypergraphs, and (2) propose a simple primal feasible heuristic that allows for a reasonably efficient inference in instances of higher-order lifted multicut problem instances defined on point trajectory hypergraphs for motion segmentation. The resulting motion segmentations improve over the state-of-the-art on the FBMS-59 dataset.

Connected through hyperedge HE-1 (induced by source paper "object segmentation tracking from generic video cues"):
  with members:
    - "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"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "fast online object tracking and segmentation a unifying approach"
    - "video object segmentation using teacher student adaptation in a human robot interaction hri setting"
    - "pixel level matching for video object segmentation using convolutional neural networks"
    - "the 2017 davis challenge on video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "3d rigid motion segmentation with mixed and unknown number of models"):
  with members:
    - "motion segmentation by exploiting complementary geometric models"
    - "a detailed rubric for motion segmentation"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-3 (induced by source paper "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"):
  with members:
    - "combining stereo disparity and optical flow for basic scene flow"
    - "mirrorflow exploiting symmetries in joint optical flow and occlusion estimation"
    - "accurate optical flow via direct cost volume processing"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "joint spatio temporal boundary detection and boundary flow prediction with a fully convolutional siamese network"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "segflow joint learning for video object segmentation and optical flow"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"

Connected through hyperedge HE-4 (induced by source paper "motion based object segmentation based on dense rgb d scene flow"):
  with members:
    - "interactive perception leveraging action in perception and perception in action"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "scene flow estimation a survey"
    - "shapenet an information rich 3d model repository"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal 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)