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 simple fast and highly accurate algorithm to recover 3d shape from 2d landmarks on a single image"
  Abstract: Three-dimensional shape reconstruction of 2D landmark points on a single image is a hallmark of human vision, but is a task that has been proven difficult for computer vision algorithms. We define a feed-forward deep neural network algorithm that can reconstruct 3D shapes from 2D landmark points almost perfectly (i.e., with extremely small reconstruction errors), even when these 2D landmarks are from a single image. Our experimental results show an improvement of up to two-fold over state-of-the-art computer vision algorithms; 3D shape reconstruction of human faces is given at a reconstruction error < .004, cars at .0022, human bodies at .022, and highly-deformable flags at an error of .0004. Our algorithm was also a top performer at the 2016 3D Face Alignment in the Wild Challenge competition (done in conjunction with the European Conference on Computer Vision, ECCV) that required the reconstruction of 3D face shape from a single image. The derived algorithm can be trained in a couple hours and testing runs at more than 1, 000 frames/s on an i7 desktop. We also present an innovative data augmentation approach that allows us to train the system efficiently with small number of samples. And the system is robust to noise (e.g., imprecise landmark points) and missing data (e.g., occluded or undetected landmark points).

Connected through hyperedge HE-1 (induced by source paper "sim2real transfer learning for 3d pose estimation motion to the rescue"):
  with members:
    - "does computer vision matter for action"
    - "learning the depths of moving people by watching frozen people"
    - "an annotation saved is an annotation earned using fully synthetic training for object instance detection"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "ordinal depth supervision for 3d human pose estimation"
    - "learning human optical flow"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "unsupervised learning of object landmarks through conditional image generation"

Connected through hyperedge HE-2 (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"
    - "structure from recurrent motion from rigidity to recurrency"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "feature boosting network for 3d pose estimation"):
  with members:
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "learning to estimate 3d hand pose from single rgb images"
    - "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"
    - "ganerated hands for real time 3d hand tracking from monocular rgb"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-4 (induced by source paper "holistic scene understanding single view 3d holistic scene parsing and human pose estimation with human object interaction and physical commonsense"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "learning human object interactions by graph parsing neural networks"
    - "cooperative holistic scene understanding unifying 3d object layout and camera pose estimation"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "lifting from the deep convolutional 3d pose estimation from a single image"

Connected through hyperedge HE-5 (induced by source paper "domain transfer for 3d pose estimation from color images without manual annotations"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "keep it unreal bridging the realism gap for 2 5d recognition with geometry priors only"
    - "unsupervised domain adaptation with adversarial residual transform networks"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "cross modal deep variational hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"

Connected through hyperedge HE-6 (induced by source paper "lifting from the deep convolutional 3d pose estimation from a single image"):
  with members:
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "convolutional pose machines"
    - "learning to fuse 2d and 3d image cues for monocular body pose estimation"
    - "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"
    - "structured prediction of 3d human pose with deep neural networks"
    - "single image 3d interpreter network"
    - "deep kinematic pose regression"

Connected through hyperedge HE-7 (induced by source paper "learning to estimate 3d hand pose from single rgb images"):
  with members:
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "deep multitask architecture for integrated 2d and 3d human sensing"
    - "convolutional pose machines"
    - "training a feedback loop for hand pose estimation"
    - "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"
    - "3d hand pose tracking and estimation using stereo matching"
    - "3d human pose estimation 2d pose estimation matching"

Connected through hyperedge HE-8 (induced by source paper "deep structure for end to end inverse rendering"):
  with members:
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "3d reconstruction of simple objects from a single view silhouette image"
    - "perspective transformer nets learning single view 3d object reconstruction without 3d supervision"
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"

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)