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: "synthetic occlusion augmentation with volumetric heatmaps for the 2018 eccv posetrack challenge on 3d human pose estimation"
  Abstract: In this paper we present our winning entry at the 2018 ECCV PoseTrack Challenge on 3D human pose estimation. Using a fully-convolutional backbone architecture, we obtain volumetric heatmaps per body joint, which we convert to coordinates using soft-argmax. Absolute person center depth is estimated by a 1D heatmap prediction head. The coordinates are back-projected to 3D camera space, where we minimize the L1 loss. Key to our good results is the training data augmentation with randomly placed occluders from the Pascal VOC dataset. In addition to reaching first place in the Challenge, our method also surpasses the state-of-the-art on the full Human3.6M benchmark among methods that use no additional pose datasets in training. Code for applying synthetic occlusions is availabe at this https URL.

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 "visual person understanding through multi task and multi dataset learning"):
  with members:
    - "improved person re identification based on saliency and semantic parsing with deep neural network models"
    - "casenet content adaptive scale interaction networks for scene parsing"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "panoptic feature pyramid networks"
    - "improving person re identification by attribute and identity learning"
    - "devil in the details towards accurate single and multiple human parsing"
    - "learning discriminative features with multiple granularities for person re identification"
    - "group normalization"

Connected through hyperedge HE-3 (induced by source paper "learning to generate synthetic data via compositing"):
  with members:
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "spatially transformed adversarial examples"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "loss rank mining a general hard example mining method for real time detectors"
    - "modeling visual context is key to augmenting object detection datasets"
    - "unsupervised hard example mining from videos for improved object detection"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "can 3d pose be learned from 2d projections alone"

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"
    - "3d human pose estimation with 2d marginal heatmaps"

Connected through hyperedge HE-5 (induced by source paper "absposelifter absolute 3d human pose lifting network from a single noisy 2d human pose"):
  with members:
    - "posefix model agnostic general human pose refinement network"
    - "simple baselines for human pose estimation and tracking"
    - "3d human pose estimation in the wild by adversarial learning"
    - "numerical coordinate regression with convolutional neural networks"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

Connected through hyperedge HE-6 (induced by source paper "absolute human pose estimation with depth prediction network"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "fbi pose towards bridging the gap between 2d images and 3d human poses using forward or backward information"
    - "drpose3d depth ranking in 3d human pose estimation"
    - "it s all relative monocular 3d human pose estimation from weakly supervised data"
    - "megadepth learning single view depth prediction from internet photos"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "single shot multi person 3d pose estimation from monocular rgb"

Connected through hyperedge HE-7 (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"
    - "3d human pose estimation with 2d marginal heatmaps"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "orinet a fully convolutional network for 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"

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)