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 monocular surface reconstruction"
  Abstract: This paper reports on a novel template-free monocular non-rigid surface reconstruction approach. Existing techniques using motion and deformation cues rely on multiple prior assumptions, are often computationally expensive and do not perform equally well across the variety of data sets. In contrast, the proposed Scalable Monocular Surface Reconstruction (SMSR) combines strengths of several algorithms, i.e., it is scalable with the number of points, can handle sparse and dense settings as well as different types of motions and deformations. We estimate camera pose by singular value thresholding and proximal gradient. Our formulation adopts alternating direction method of multipliers which converges in linear time for large point track matrices. In the proposed SMSR, trajectory space constraints are integrated by smoothing of the measurement matrix. In the extensive experiments, SMSR is demonstrated to consistently achieve state-of-the-art accuracy on a wide variety of data sets.

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 "structure from articulated motion an accurate and stable monocular 3d reconstruction approach without training data"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "ordinal depth supervision for 3d human pose estimation"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"
    - "exploiting temporal information for 3d human pose estimation"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "end to end recovery of human shape and pose"

Connected through hyperedge HE-3 (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"
    - "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-4 (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"
    - "a benchmark and evaluation of non rigid structure from motion"
    - "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"

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"
    - "a benchmark and evaluation of non rigid structure from motion"
    - "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames"
    - "capturing dynamic textured surfaces of moving targets"

Connected through hyperedge HE-6 (induced by source paper "hdm net monocular non rigid 3d reconstruction with learned deformation model"):
  with members:
    - "geometry aware network for non rigid shape prediction from a single view"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "octnetfusion learning depth fusion from data"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "accurate 3d reconstruction of dynamic scenes from monocular image sequences with severe occlusions"
    - "end to end 3d face reconstruction with deep neural networks"
    - "better together joint reasoning for non rigid 3d reconstruction with specularities and shading"
    - "lifting from the deep convolutional 3d pose estimation from a single image"

Connected through hyperedge HE-7 (induced by source paper "a benchmark and evaluation of non rigid structure from motion"):
  with members:
    - "practical matrix completion and corruption recovery using proximal alternating robust subspace minimization"

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)