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: "human pose estimation using deep consensus voting"
  Abstract: In this paper we consider the problem of human pose estimation from a single still image. We propose a novel approach where each location in the image votes for the position of each keypoint using a convolutional neural net. The voting scheme allows us to utilize information from the whole image, rather than rely on a sparse set of keypoint locations. Using dense, multi-target votes, not only produces good keypoint predictions, but also enables us to compute image-dependent joint keypoint probabilities by looking at consensus voting. This differs from most previous methods where joint probabilities are learned from relative keypoint locations and are independent of the image. We finally combine the keypoints votes and joint probabilities in order to identify the optimal pose configuration. We show our competitive performance on the MPII Human Pose and Leeds Sports Pose datasets.

Connected through hyperedge HE-1 (induced by source paper "toward fast and accurate human pose estimation via soft gated skip connections"):
  with members:
    - "anchor loss modulating loss scale based on prediction difficulty"
    - "deep high resolution representation learning for human pose estimation"
    - "quantized densely connected u nets for efficient landmark localization"
    - "cu net coupled u nets"
    - "multi scale structure aware network for human pose estimation"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "deep high resolution representation learning for human pose estimation"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "pose flow efficient online pose tracking"
    - "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"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "multiposenet fast multi person pose estimation using pose residual network"

Connected through hyperedge HE-3 (induced by source paper "multi task deep learning for real time 3d human pose estimation and action recognition"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "lifting from the deep convolutional 3d pose estimation from a single image"

Connected through hyperedge HE-4 (induced by source paper "human pose estimation with spatial contextual information"):
  with members:
    - "path aggregation network for instance segmentation"
    - "situation recognition with graph neural networks"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "multi context attention for human pose estimation"
    - "mask r cnn"
    - "convolutional pose machines"

Connected through hyperedge HE-5 (induced by source paper "cascade feature aggregation for human pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "human pose estimation with spatial contextual information"
    - "quantized densely connected u nets for efficient landmark localization"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "multi scale structure aware network for human pose estimation"
    - "learning to refine human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "learning feature pyramids for human pose estimation"

Connected through hyperedge HE-6 (induced by source paper "anchor loss modulating loss scale based on prediction difficulty"):
  with members:
    - "multi scale structure aware network for human pose estimation"
    - "smooth loss functions for deep top k classification"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "focal loss for dense object detection"

Connected through hyperedge HE-7 (induced by source paper "pifpaf composite fields for human pose estimation"):
  with members:
    - "learning to refine human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "densepose dense human pose estimation in the wild"
    - "towards accurate multi person pose estimation in the wild"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "cascaded pyramid network for multi person pose estimation"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-8 (induced by source paper "simple and lightweight human pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "deep high resolution representation learning for human pose estimation"
    - "multi scale structure aware network for human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "non local neural networks"
    - "learning feature pyramids for human pose estimation"
    - "squeeze and excitation networks"

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)