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 recurrent motion from rigidity to recurrency"
  Abstract: This paper proposes a new method for Non-Rigid Structure-from-Motion (NRSfM) from a long monocular video sequence observing a non-rigid object performing recurrent and possibly repetitive dynamic action. Departing from the traditional idea of using linear low-order or lowrank shape model for the task of NRSfM, our method exploits the property of shape recurrency (i.e., many deforming shapes tend to repeat themselves in time). We show that recurrency is in fact a generalized rigidity. Based on this, we reduce NRSfM problems to rigid ones provided that certain recurrency condition is satisfied. Given such a reduction, standard rigid-SfM techniques are directly applicable (without any change) to the reconstruction of non-rigid dynamic shapes. To implement this idea as a practical approach, this paper develops efficient algorithms for automatic recurrency detection, as well as camera view clustering via a rigidity-check. Experiments on both simulated sequences and real data demonstrate the effectiveness of the method. Since this paper offers a novel perspective on rethinking structure-from-motion, we hope it will inspire other new problems in the field.

Connected through hyperedge HE-1 (induced by source paper "exploiting temporal context for 3d human pose estimation in the wild"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "image based synthesis for deep 3d human pose estimation"
    - "learning 3d human dynamics from video"
    - "how robust is 3d human pose estimation to occlusion"
    - "sfv reinforcement learning of physical skills from videos"
    - "mosculp interactive visualization of shape and time"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"

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"
    - "a benchmark and evaluation of non rigid structure from motion"
    - "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 "repetition estimation"):
  with members:
    - "real world repetition estimation by div grad and curl"
    - "quasi periodicity quantification in video data using topology"
    - "learning motion patterns in videos"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "epicflow edge preserving interpolation of correspondences for optical flow"

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)