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: "a versatile scene model with differentiable visibility applied to generative pose estimation"
  Abstract: Generative reconstruction methods compute the 3D configuration (such as pose and/or geometry) of a shape by optimizing the overlap of the projected 3D shape model with images. Proper handling of occlusions is a big challenge, since the visibility function that indicates if a surface point is seen from a camera can often not be formulated in closed form, and is in general discrete and non-differentiable at occlusion boundaries. We present a new scene representation that enables an analytically differentiable closed-form formulation of surface visibility. In contrast to previous methods, this yields smooth, analytically differentiable, and efficient to optimize pose similarity energies with rigorous occlusion handling, fewer local minima, and experimentally verified improved convergence of numerical optimization. The underlying idea is a new image formation model that represents opaque objects by a translucent medium with a smooth Gaussian density distribution which turns visibility into a smooth phenomenon. We demonstrate the advantages of our versatile scene model in several generative pose estimation problems, namely marker-less multi-object pose estimation, marker-less human motion capture with few cameras, and image-based 3D geometry estimation.

Connected through hyperedge HE-1 (induced by source paper "differentiable visual computing"):
  with members:
    - "on the convergence of adam and beyond"
    - "elimination of all bad local minima in deep learning"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "stochastic gradient descent with biased but consistent gradient estimators"
    - "line drawings from 3d models"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "tangent automatic differentiation using source code transformation in python"

Connected through hyperedge HE-2 (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"
    - "structure from articulated motion an accurate and stable monocular 3d reconstruction approach without training data"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "deformation capture via soft and stretchable sensor arrays"):
  with members:
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "learning to fuse 2d and 3d image cues for monocular body pose estimation"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"
    - "sparseness meets deepness 3d human pose estimation from monocular video"
    - "adam a method for stochastic optimization"
    - "joint training of a convolutional network and a graphical model for human pose estimation"

Connected through hyperedge HE-4 (induced by source paper "mo2cap2 real time mobile 3d motion capture with a cap mounted fisheye camera"):
  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"
    - "harvesting multiple views for marker less 3d human pose annotations"
    - "learning from synthetic humans"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "generating multiple diverse hypotheses for human 3d pose consistent with 2d joint detections"
    - "mask r cnn"

Connected through hyperedge HE-5 (induced by source paper "deep inertial poser learning to reconstruct human pose from sparse inertial measurements in real time"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "doublefusion real time capture of human performances with inner body shapes from a single depth sensor"
    - "learning human motion models for long term predictions"
    - "single shot multi person 3d pose estimation from monocular rgb"
    - "end to end recovery of human shape and pose"
    - "towards accurate markerless human shape and pose estimation over time"

Connected through hyperedge HE-6 (induced by source paper "unsupervised learning of shape and pose with differentiable point clouds"):
  with members:
    - "3d interpreter networks for viewer centered wireframe modeling"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "foldingnet interpretable unsupervised learning on 3d point clouds"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "neural 3d mesh renderer"
    - "learning shape abstractions by assembling volumetric primitives"

Connected through hyperedge HE-7 (induced by source paper "vnect real time 3d human pose estimation with a single rgb camera"):
  with members:
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "coarse to fine volumetric prediction for single image 3d human pose"
    - "learning to fuse 2d and 3d image cues for monocular body pose estimation"
    - "egocap egocentric marker less motion capture with two fisheye cameras"
    - "mocap guided data augmentation for 3d pose estimation in the wild"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"

Connected through hyperedge HE-8 (induced by source paper "egocap egocentric marker less motion capture with two fisheye cameras"):
  with members:
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "detecting engagement in egocentric video"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "going deeper into first person activity recognition"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "learning action maps of large environments via first person vision"
    - "general automatic human shape and motion capture using volumetric contour cues"

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)