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: "dense non rigid structure from motion made easy a spatial temporal smoothness based solution"
  Abstract: This paper proposes a simple spatial-temporal smoothness based method for solving dense non-rigid structure-from-motion (NRSfM). First, we revisit the temporal smoothness and demonstrate that it can be extended to dense case directly. Second, we propose to exploit the spatial smoothness by resorting to the Laplacian of the 3D non-rigid shape. Third, to handle real world noise and outliers in measurements, we robustify the data term by using the $L_1$ norm. In this way, our method could robustly exploit both spatial and temporal smoothness effectively and make dense non-rigid reconstruction easy. Our method is very easy to implement, which involves solving a series of least squares problems. Experimental results on both synthetic and real image dense NRSfM tasks show that the proposed method outperforms state-of-the-art dense non-rigid reconstruction methods.

Connected through hyperedge HE-1 (induced by source paper "jumping manifolds geometry aware dense non rigid structure from motion"):
  with members:
    - "a simple prior free method for non rigid structure from motion factorization revisited"
    - "non rigid structure from motion prior free factorization method revisited"
    - "dense depth estimation of a complex dynamic scene without explicit 3d motion estimation"
    - "scalable dense non rigid structure from motion a grassmannian perspective"
    - "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "scalable dense monocular surface reconstruction"
    - "on differentiating parameterized argmin and argmax problems with application to bi level optimization"

Connected through hyperedge HE-2 (induced by source paper "scalable dense non rigid structure from motion a grassmannian perspective"):
  with members:
    - "a benchmark and evaluation of non rigid structure from motion"
    - "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames"
    - "low rank representation on grassmann manifolds an extrinsic perspective"
    - "robust recovery of subspace structures by low rank representation"

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)