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: "monoperfcap human performance capture from monocular video"
  Abstract: We present the first marker-less approach for temporally coherent 3D performance capture of a human with general clothing from monocular video. Our approach reconstructs articulated human skeleton motion as well as medium-scale non-rigid surface deformations in general scenes. Human performance capture is a challenging problem due to the large range of articulation, potentially fast motion, and considerable non-rigid deformations, even from multi-view data. Reconstruction from monocular video alone is drastically more challenging, since strong occlusions and the inherent depth ambiguity lead to a highly ill-posed reconstruction problem. We tackle these challenges by a novel approach that employs sparse 2D and 3D human pose detections from a convolutional neural network using a batch-based pose estimation strategy. Joint recovery of per-batch motion allows to resolve the ambiguities of the monocular reconstruction problem based on a low dimensional trajectory subspace. In addition, we propose refinement of the surface geometry based on fully automatically extracted silhouettes to enable medium-scale non-rigid alignment. We demonstrate state-of-the-art performance capture results that enable exciting applications such as video editing and free viewpoint video, previously infeasible from monocular video. Our qualitative and quantitative evaluation demonstrates that our approach significantly outperforms previous monocular methods in terms of accuracy, robustness and scene complexity that can be handled.

Connected through hyperedge HE-1 (induced by source paper "learning 3d human dynamics from video"):
  with members:
    - "deep lip reading a comparison of models and an online application"
    - "reticam real time human performance capture from monocular video"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "bodynet volumetric inference of 3d human body shapes"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "deep autoencoder for combined human pose estimation and body model upscaling"

Connected through hyperedge HE-2 (induced by source paper "monocular total capture posing face body and hands in the wild"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "headon real time reenactment of human portrait videos"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "bodynet volumetric inference of 3d human body shapes"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "drpose3d depth ranking in 3d human pose estimation"

Connected through hyperedge HE-3 (induced by source paper "learning to estimate 3d human pose and shape from a single color image"):
  with members:
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "towards accurate markerless human shape and pose estimation over time"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "compositional human pose regression"
    - "unite the people closing the loop between 3d and 2d human representations"

Connected through hyperedge HE-4 (induced by source paper "video based reconstruction of 3d people models"):
  with members:
    - "detailed accurate human shape estimation from clothed 3d scan sequences"
    - "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"
    - "unite the people closing the loop between 3d and 2d human representations"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "deep multitask architecture for integrated 2d and 3d human sensing"
    - "convolutional pose machines"

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)