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: "accurate 3d reconstruction of dynamic scenes from monocular image sequences with severe occlusions"
  Abstract: The paper introduces an accurate solution to dense orthographic Non-Rigid Structure from Motion (NRSfM) in scenarios with severe occlusions or, likewise, inaccurate correspondences. We integrate a shape prior term into variational optimisation framework. It allows to penalize irregularities of the time-varying structure on the per-pixel level if correspondence quality indicator such as an occlusion tensor is available. We make a realistic assumption that several non-occluded views of the scene are sufficient to estimate an initial shape prior, though the entire observed scene may exhibit non-rigid deformations. Experiments on synthetic and real image data show that the proposed framework significantly outperforms state of the art methods for correspondence establishment in combination with the state of the art NRSfM methods. Together with the profound insights into optimisation methods, implementation details for heterogeneous platforms are provided.

Connected through hyperedge HE-1 (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"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "scalable dense monocular surface reconstruction"

Connected through hyperedge HE-2 (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"
    - "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"
    - "unrestricted facial geometry reconstruction using image to image translation"

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)