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: "optical flow based 3d human motion estimation from monocular video"
  Abstract: We present a generative method to estimate 3D human motion and body shape from monocular video. Under the assumption that starting from an initial pose optical flow constrains subsequent human motion, we exploit flow to find temporally coherent human poses of a motion sequence. We estimate human motion by minimizing the difference between computed flow fields and the output of an artificial flow renderer. A single initialization step is required to estimate motion over multiple frames. Several regularization functions enhance robustness over time. Our test scenarios demonstrate that optical flow effectively regularizes the under-constrained problem of human shape and motion estimation from monocular video.

Connected through hyperedge HE-1 (induced by source paper "deep learning vs traditional computer vision"):
  with members:
    - "recurrent convolutional fusion for rgb d object recognition"
    - "hybrid bayesian eigenobjects combining linear subspace and deep network methods for 3d robot vision"
    - "hybrid scene compression for visual localization"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "the effectiveness of data augmentation in image classification using deep learning"

Connected through hyperedge HE-2 (induced by source paper "bodynet volumetric inference of 3d human body shapes"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "densereg fully convolutional dense shape regression in the wild"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "densepose dense human pose estimation in the wild"
    - "octnetfusion learning depth fusion from data"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"

Connected through hyperedge HE-3 (induced by source paper "learning 3d human pose from structure and motion"):
  with members:
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "compositional human pose regression"
    - "learning from synthetic humans"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "long short term memory kalman filters recurrent neural estimators for pose regularization"
    - "generating multiple diverse hypotheses for human 3d pose consistent with 2d joint detections"
    - "recurrent 3d pose sequence machines"

Connected through hyperedge HE-4 (induced by source paper "self supervised learning of motion capture"):
  with members:
    - "adversarial inverse graphics networks learning 2d to 3d lifting and image to image translation from unpaired supervision"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "unsupervised learning of depth and ego motion from video"
    - "learning from synthetic humans"
    - "sfm net learning of structure and motion from video"
    - "convolutional pose machines"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-5 (induced by source paper "a kinematic chain space for monocular motion capture"):
  with members:
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "end to end recovery of human shape and pose"
    - "unite the people closing the loop between 3d and 2d human representations"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "sparse inertial poser automatic 3d human pose estimation from sparse imus"
    - "coarse to fine volumetric prediction for single image 3d human pose"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"
    - "deepercut a deeper stronger and faster multi person pose estimation model"

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)