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: "multiframe scene flow with piecewise rigid motion"
  Abstract: We introduce a novel multiframe scene flow approach that jointly optimizes the consistency of the patch appearances and their local rigid motions from RGB-D image sequences. In contrast to the competing methods, we take advantage of an oversegmentation of the reference frame and robust optimization techniques. We formulate scene flow recovery as a global non-linear least squares problem which is iteratively solved by a damped Gauss-Newton approach. As a result, we obtain a qualitatively new level of accuracy in RGB-D based scene flow estimation which can potentially run in real-time. Our method can handle challenging cases with rigid, piecewise rigid, articulated and moderate non-rigid motion, and does not rely on prior knowledge about the types of motions and deformations. Extensive experiments on synthetic and real data show that our method outperforms state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "deep part induction from articulated object pairs"):
  with members:
    - "splatnet sparse lattice networks for point cloud processing"
    - "motion based object segmentation based on dense rgb d scene flow"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"):
  with members:
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "pyramid stereo matching network"
    - "optical flow in mostly rigid scenes"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "learning from synthetic humans"
    - "demon depth and motion network for learning monocular stereo"

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)