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: "scalable dense non rigid structure from motion a grassmannian perspective"
  Abstract: This paper addresses the task of dense non-rigid structure-from-motion (NRSfM) using multiple images. State-of-the-art methods to this problem are often hurdled by scalability, expensive computations, and noisy measurements. Further, recent methods to NRSfM usually either assume a small number of sparse feature points or ignore local non-linearities of shape deformations, and thus cannot reliably model complex non-rigid deformations. To address these issues, in this paper, we propose a new approach for dense NRSfM by modeling the problem on a Grassmann manifold. Specifically, we assume the complex non-rigid deformations lie on a union of local linear subspaces both spatially and temporally. This naturally allows for a compact representation of the complex non-rigid deformation over frames. We provide experimental results on several synthetic and real benchmark datasets. The procured results clearly demonstrate that our method, apart from being scalable and more accurate than state-of-the-art methods, is also more robust to noise and generalizes to highly non-linear deformations.

Connected through hyperedge HE-1 (induced by source paper "structure from articulated motion accurate and stable monocular 3d reconstruction without training data"):
  with members:
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "ordinal depth supervision for 3d human pose estimation"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"

Connected through hyperedge HE-2 (induced by source paper "web stereo video supervision for depth prediction from dynamic scenes"):
  with members:
    - "learning the depths of moving people by watching frozen people"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deeplens shallow depth of field from a single image"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "deepmvs learning multi view stereopsis"
    - "learning monocular depth by distilling cross domain stereo networks"

Connected through hyperedge HE-3 (induced by source paper "distill knowledge from nrsfm for weakly supervised 3d pose learning"):
  with members:
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "deep interpretable non rigid structure from motion"
    - "ordinal depth supervision for 3d human pose estimation"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "apollocar3d a large 3d car instance understanding benchmark for autonomous driving"
    - "3d human pose estimation in the wild by adversarial learning"
    - "can 3d pose be learned from 2d projections alone"
    - "learning monocular 3d human pose estimation from multi view images"

Connected through hyperedge HE-4 (induced by source paper "c3dpo canonical 3d pose networks for non rigid structure from motion"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "deep high resolution representation learning for human pose estimation"
    - "ordinal depth supervision for 3d human pose estimation"
    - "learning category specific mesh reconstruction from image collections"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "unsupervised adversarial learning of 3d human pose from 2d joint locations"
    - "learning 3d human dynamics from video"
    - "can 3d pose be learned from 2d projections alone"

Connected through hyperedge HE-5 (induced by source paper "dense depth estimation of a complex dynamic scene without explicit 3d motion estimation"):
  with members:
    - "jumping manifolds geometry aware dense non rigid structure from motion"
    - "a simple prior free method for non rigid structure from motion factorization revisited"
    - "non rigid structure from motion prior free factorization method revisited"
    - "digging into self supervised monocular depth estimation"
    - "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "maximizing rigidity revisited a convex programming approach for generic 3d shape reconstruction from multiple perspective views"

Connected through hyperedge HE-6 (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"
    - "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"
    - "dense non rigid structure from motion made easy a spatial temporal smoothness based solution"
    - "on differentiating parameterized argmin and argmax problems with application to bi level optimization"

Connected through hyperedge HE-7 (induced by source paper "deep interpretable non rigid structure from motion"):
  with members:
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "estimating depth from rgb and sparse sensing"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "parse geometry from a line monocular depth estimation with partial laser observation"
    - "single image 3d interpreter network"
    - "shapenet an information rich 3d model repository"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-8 (induced by source paper "a simple prior free method for non rigid structure from motion factorization revisited"):
  with members:
    - "jumping manifolds geometry aware dense non rigid structure from motion"
    - "dense depth estimation of a complex dynamic scene without explicit 3d motion estimation"
    - "a benchmark and evaluation of non rigid structure from motion"
    - "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames"
    - "analyzing the weighted nuclear norm minimization and nuclear norm minimization based on group sparse representation"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "robust recovery of subspace structures by low rank representation"
    - "weighted algorithms for compressed sensing and matrix completion"

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)