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: "structure from articulated motion an accurate and stable monocular 3d reconstruction approach without training data"
  Abstract: Recovery of articulated 3D structure from 2D observations is a challenging computer vision problem with many applications. Current learning-based approaches achieve state-of-the-art performance on public benchmarks but are limited to the specific types of objects and motions covered by the training datasets. Model-based approaches do not rely on training data but show lower accuracy on public benchmarks. In this paper, we introduce a new model-based method called Structure from Articulated Motion (SfAM). SfAM includes a new articulated structure term which ensures consistency of bone lengths throughout the whole image sequence and recovers a scene-specific configuration of the articulated structure. The proposed approach is highly robust to noisy 2D annotations, generalizes to arbitrary objects and motion types and does not rely on training data. It achieves state-of-the-art accuracy and scales across different scenarios which is shown in extensive experiments on public benchmarks and real video sequences.

Connected through hyperedge HE-1 (induced by source paper "eventcap monocular 3d capture of high speed human motions using an event camera"):
  with members:
    - "event based vision a survey"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "temporal upsampling of depth maps using a hybrid camera"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "asynchronous photometric feature tracking using events and frames"
    - "bringing a blurry frame alive at high frame rate with an event camera"
    - "doublefusion real time capture of human performances with inner body shapes from a single depth sensor"

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"
    - "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"
    - "scalable dense monocular surface reconstruction"

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)