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: "3d human pose estimation with 2d marginal heatmaps"
  Abstract: Automatically determining three-dimensional human pose from monocular RGB image data is a challenging problem. The two-dimensional nature of the input results in intrinsic ambiguities which make inferring depth particularly difficult. Recently, researchers have demonstrated that the flexible statistical modelling capabilities of deep neural networks are sufficient to make such inferences with reasonable accuracy. However, many of these models use coordinate output techniques which are memory-intensive, not differentiable, and/or do not spatially generalise well. We propose improvements to 3D coordinate prediction which avoid the aforementioned undesirable traits by predicting 2D marginal heatmaps under an augmented soft-argmax scheme. Our resulting model, MargiPose, produces visually coherent heatmaps whilst maintaining differentiability. We are also able to achieve state-of-the-art accuracy on publicly available 3D human pose estimation data.

Connected through hyperedge HE-1 (induced by source paper "metric scale truncation robust heatmaps for 3d human pose estimation"):
  with members:
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "ordinal depth supervision for 3d human pose estimation"
    - "multi scale structure aware network for human pose estimation"
    - "yolov3 an incremental improvement"
    - "video based reconstruction of 3d people models"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "how robust is 3d human pose estimation to occlusion"
    - "3d human pose estimation in the wild by adversarial learning"

Connected through hyperedge HE-2 (induced by source paper "unipose unified human pose estimation in single images and videos"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "cascade feature aggregation for human pose estimation"
    - "human pose estimation with spatial contextual information"
    - "deep high resolution representation learning for human pose estimation"
    - "lighttrack a generic framework for online top down human pose tracking"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "multi scale structure aware network for human pose estimation"

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"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "deepfuse an imu aware network for real time 3d human pose estimation from multi view image"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "fast and robust multi person 3d pose estimation from multiple views"
    - "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
    - "a generalizable approach for multi view 3d human pose regression"
    - "multi scale structure aware network for human pose estimation"
    - "deep autoencoder for combined human pose estimation and body model upscaling"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "numerical coordinate regression with convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "synthetic occlusion augmentation with volumetric heatmaps for the 2018 eccv posetrack challenge on 3d human pose estimation"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "an occluded stacked hourglass approach to facial landmark localization and occlusion estimation"
    - "multi scale structure aware network for human pose estimation"
    - "yolov3 an incremental improvement"
    - "modeling visual context is key to augmenting object detection datasets"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "how robust is 3d human pose estimation to occlusion"
    - "numerical coordinate regression with convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "smplr deep smpl reverse for 3d human pose and shape recovery"):
  with members:
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "3d human pose estimation in the wild by adversarial learning"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "orinet a fully convolutional network for 3d human pose estimation"
    - "synthetic occlusion augmentation with volumetric heatmaps for the 2018 eccv posetrack challenge on 3d human pose estimation"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"

Connected through hyperedge HE-7 (induced by source paper "how robust is 3d human pose estimation to occlusion"):
  with members:
    - "an occluded stacked hourglass approach to facial landmark localization and occlusion estimation"
    - "multi scale structure aware network for human pose estimation"
    - "modeling visual context is key to augmenting object detection datasets"
    - "numerical coordinate regression with convolutional neural networks"
    - "random erasing data augmentation"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "compositional human pose regression"
    - "integral human pose regression"

Connected through hyperedge HE-8 (induced by source paper "human pose regression by combining indirect part detection and contextual information"):
  with members:
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "simple baselines for human pose estimation and tracking"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "compositional human pose regression"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "multi context attention for human pose estimation"

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)