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: "joint spatio temporal boundary detection and boundary flow prediction with a fully convolutional siamese network"
  Abstract: This paper addresses a new problem of joint object boundary detection and boundary motion estimation in videos, which we named boundary flow estimation. Boundary flow is an important mid-level visual cue as boundaries characterize spatial extents of objects, and the flow indicates motions and interactions of objects. Yet, most prior work on motion estimation has focused on dense object motion or feature points that may not necessarily reside on boundaries. For boundary flow estimation, we specify a new fully convolutional Siamese network (FCSN) that jointly estimates object-level boundaries in two consecutive frames. Boundary correspondences in the two frames are predicted by the same FCSN with a new, unconventional deconvolution approach. Finally, the boundary flow estimate is improved with an edgelet based filtering. Evaluation is conducted on three tasks: boundary detection in videos, boundary flow estimation, and optical flow estimation. On boundary detection, we achieve the state-of-the-art performance on the benchmark VSB100 dataset. On boundary flow estimation, we present the first results on the Sintel training dataset. For optical flow estimation, we run the recent approach CPM-Flow but on the augmented input with our boundary flow matches, and achieve significant performance improvement on the Sintel benchmark.

Connected through hyperedge HE-1 (induced by source paper "trifocal relative pose from lines at points and its efficient solution"):
  with members:
    - "resultant based incremental recovery of camera pose from pairwise matches"
    - "stereo relative pose from line and point feature triplets"
    - "beyond gr obner bases basis selection for minimal solvers"
    - "a line point unified solution to relative camera pose estimation"
    - "on some properties of calibrated trifocal tensors"
    - "from multiview image curves to 3d drawings"
    - "multiview differential geometry of curves"

Connected through hyperedge HE-2 (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"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "segflow joint learning for video object segmentation and optical flow"
    - "higher order minimum cost lifted multicuts for motion segmentation"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"

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)