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: "lcr net multi person 2d and 3d pose detection in natural images"
  Abstract: We propose an end-to-end architecture for joint 2D and 3D human pose estimation in natural images. Key to our approach is the generation and scoring of a number of pose proposals per image, which allows us to predict 2D and 3D poses of multiple people simultaneously. Hence, our approach does not require an approximate localization of the humans for initialization. Our Localization-Classification-Regression architecture, named LCR-Net, contains 3 main components: 1) the pose proposal generator that suggests candidate poses at different locations in the image; 2) a classifier that scores the different pose proposals; and 3) a regressor that refines pose proposals both in 2D and 3D. All three stages share the convolutional feature layers and are trained jointly. The final pose estimation is obtained by integrating over neighboring pose hypotheses, which is shown to improve over a standard non maximum suppression algorithm. Our method recovers full-body 2D and 3D poses, hallucinating plausible body parts when the persons are partially occluded or truncated by the image boundary. Our approach significantly outperforms the state of the art in 3D pose estimation on Human3.6M, a controlled environment. Moreover, it shows promising results on real images for both single and multi-person subsets of the MPII 2D pose benchmark.

Connected through hyperedge HE-1 (induced by source paper "from kinematics to dynamics estimating center of pressure and base of support from video frames of human motion"):
  with members:
    - "movement science needs different pose tracking algorithms"
    - "densepose dense human pose estimation in the wild"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "compositional human pose regression"
    - "improved training of wasserstein gans"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "stacked hourglass networks for human pose estimation"
    - "coarse to fine volumetric prediction for single image 3d human pose"

Connected through hyperedge HE-2 (induced by source paper "animepose multi person 3d pose estimation and animation"):
  with members:
    - "trajectory prediction by coupling scene lstm with human movement lstm"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "pose flow efficient online pose tracking"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "single shot multi person 3d pose estimation from monocular rgb"
    - "posetrack a benchmark for human pose estimation and tracking"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "vnect real time 3d human pose estimation with a single rgb camera"

Connected through hyperedge HE-3 (induced by source paper "xnect real time multi person 3d human pose estimation with a single rgb camera"):
  with members:
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "expressive body capture 3d hands face and body from a single image"
    - "ordinal depth supervision for 3d human pose estimation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "regularized evolution for image classifier architecture search"
    - "video based reconstruction of 3d people models"

Connected through hyperedge HE-4 (induced by source paper "camera distance aware top down approach for 3d multi person pose estimation from a single rgb image"):
  with members:
    - "multi scale aggregation r cnn for 2d multi person pose estimation"
    - "deep attention based classification network for robust depth prediction"
    - "posefix model agnostic general human pose refinement network"
    - "deep ordinal regression network for monocular depth estimation"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "simple baselines for human pose estimation and tracking"
    - "3d human pose estimation in the wild by adversarial learning"
    - "multiposenet fast multi person pose estimation using pose residual network"

Connected through hyperedge HE-5 (induced by source paper "hierarchical joint scene coordinate classification and regression for visual localization"):
  with members:
    - "fine grained segmentation networks self supervised segmentation for improved long term visual localization"
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "neural guided ransac learning where to sample model hypotheses"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "expert sample consensus applied to camera re localization"
    - "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation"
    - "cascaded parallel filtering for memory efficient image based localization"
    - "real time rgb d camera pose estimation in novel scenes using a relocalisation cascade"

Connected through hyperedge HE-6 (induced by source paper "mimetics towards understanding human actions out of context"):
  with members:
    - "repair removing representation bias by dataset resampling"
    - "star net action recognition using spatio temporal activation reprojection"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "action machine rethinking action recognition in trimmed videos"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "pose based two stream relational networks for action recognition in videos"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"

Connected through hyperedge HE-7 (induced by source paper "xr egopose egocentric 3d human pose from an hmd camera"):
  with members:
    - "learning to estimate 3d human pose and shape from a single color image"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "can 3d pose be learned from 2d projections alone"
    - "exploiting temporal information for 3d human pose estimation"
    - "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"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "sparse representation for 3d shape estimation a convex relaxation approach"

Connected through hyperedge HE-8 (induced by source paper "densebody directly regressing dense 3d human pose and shape from a single color image"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "synthesizing images of humans in unseen poses"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "3d human body reconstruction from a single image via volumetric regression"
    - "3d human pose estimation in the wild by adversarial learning"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "dense pose transfer"

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)