human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "jumping manifolds geometry aware dense non rigid structure from motion"):
  Abstract: Given dense image feature correspondences of a non-rigidly moving object across multiple frames, this paper proposes an algorithm to estimate its 3D shape for each frame. To solve this problem accurately, the recent state-of-the-art algorithm reduces this task to set of local linear subspace reconstruction and clustering problem using Grassmann manifold representation \cite{kumar2018scalable}. Unfortunately, their method missed on some of the critical issues associated with the modeling of surface deformations, for e.g., the dependence of a local surface deformation on its neighbors. Furthermore, their representation to group high dimensional data points inevitably introduce the drawbacks of categorizing samples on the high-dimensional Grassmann manifold \cite{huang2015projection, harandi2014manifold}. Hence, to deal with such limitations with \cite{kumar2018scalable}, we propose an algorithm that jointly exploits the benefit of high-dimensional Grassmann manifold to perform reconstruction, and its equivalent lower-dimensional representation to infer suitable clusters. To accomplish this, we project each Grassmannians onto a lower-dimensional Grassmann manifold which preserves and respects the deformation of the structure w.r.t its neighbors. These Grassmann points in the lower-dimension then act as a representative for the selection of high-dimensional Grassmann samples to perform each local reconstruction. In practice, our algorithm provides a geometrically efficient way to solve dense NRSfM by switching between manifolds based on its benefit and usage. Experimental results show that the proposed algorithm is very effective in handling noise with reconstruction accuracy as good as or better than the competing methods.

Includes members:
  Member "a simple prior free method for non rigid structure from motion factorization revisited":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dense depth estimation of a complex dynamic scene without explicit 3d motion estimation")
  Member "non rigid structure from motion prior free factorization method revisited":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dense depth estimation of a complex dynamic scene without explicit 3d motion estimation")
  Member "dense depth estimation of a complex dynamic scene without explicit 3d motion estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mono sf multi view geometry meets single view depth for monocular scene flow estimation of dynamic traffic scenes")
  Member "scalable dense non rigid structure from motion a grassmannian perspective":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "structure from articulated motion accurate and stable monocular 3d reconstruction without training data")
  Member "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "pwc net cnns for optical flow using pyramid warping and cost volume":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "scalable dense monocular surface reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "structure from articulated motion accurate and stable monocular 3d reconstruction without training data")
  Member "dense non rigid structure from motion made easy a spatial temporal smoothness based solution":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scalable dense non rigid structure from motion a grassmannian perspective")

Question: Please tell me which class the source paper 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)