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: "a benchmark and evaluation of non rigid structure from motion"
  Abstract: Non-Rigid structure from motion (NRSfM), is a long standing and central problem in computer vision, allowing us to obtain 3D information from multiple images when the scene is dynamic. A main issue regarding the further development of this important computer vision topic, is the lack of high quality data sets. We here address this issue by presenting of data set compiled for this purpose, which is made publicly available, and considerably larger than previous state of the art. To validate the applicability of this data set, and provide and investigation into the state of the art of NRSfM, including potential directions forward, we here present a benchmark and a scrupulous evaluation using this data set. This benchmark evaluates 16 different methods with available code, which we argue reasonably spans the state of the art in NRSfM. We also hope, that the presented and public data set and evaluation, will provide benchmark tools for further development in this field.

Connected through hyperedge HE-1 (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"
    - "scalable dense non rigid structure from motion a grassmannian perspective"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "intrinsic dynamic shape prior for fast sequential and dense non rigid structure from motion with detection of temporally disjoint rigidity"):
  with members:
    - "ismo gan adversarial learning for monocular non rigid 3d reconstruction"
    - "structure from articulated motion an accurate and stable monocular 3d reconstruction approach without training data"
    - "structure from recurrent motion from rigidity to recurrency"
    - "geometry aware network for non rigid shape prediction from a single view"
    - "accurate 3d reconstruction of dynamic scenes from monocular image sequences with severe occlusions"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "scalable dense monocular surface reconstruction"

Connected through hyperedge HE-3 (induced by source paper "deep non rigid structure from motion with missing data"):
  with members:
    - "deep interpretable non rigid structure from motion"
    - "can 3d pose be learned from 2d projections alone"
    - "single image 3d interpreter network"
    - "sparseness meets deepness 3d human pose estimation from monocular video"
    - "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-4 (induced by source paper "non rigid structure from motion prior free factorization method revisited"):
  with members:
    - "superpixel soup monocular dense 3d reconstruction of a complex dynamic scene"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "nrmvs non rigid multi view stereo"):
  with members:
    - "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames"
    - "scalable dense monocular surface reconstruction"
    - "capturing dynamic textured surfaces of moving targets"

Connected through hyperedge HE-6 (induced by source paper "non rigid structure from motion by rank one basis shapes"):
  with members:
    - "uncalibrated non rigid factorisation by independent subspace analysis"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

Connected through hyperedge HE-7 (induced by source paper "scalable dense non rigid structure from motion a grassmannian perspective"):
  with members:
    - "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames"
    - "dense non rigid structure from motion made easy a spatial temporal smoothness based solution"
    - "low rank representation on grassmann manifolds an extrinsic perspective"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-8 (induced by source paper "uncalibrated non rigid factorisation by independent subspace analysis"):
  with members:
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

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)