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: "deepercut a deeper stronger and faster multi person pose estimation model"
  Abstract: The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people. To that end we contribute on three fronts. We propose (1) improved body part detectors that generate effective bottom-up proposals for body parts; (2) novel image-conditioned pairwise terms that allow to assemble the proposals into a variable number of consistent body part configurations; and (3) an incremental optimization strategy that explores the search space more efficiently thus leading both to better performance and significant speed-up factors. Evaluation is done on two single-person and two multi-person pose estimation benchmarks. The proposed approach significantly outperforms best known multi-person pose estimation results while demonstrating competitive performance on the task of single person pose estimation. Models and code available at this http URL

Connected through hyperedge HE-1 (induced by source paper "higherhrnet scale aware representation learning for bottom up human pose estimation"):
  with members:
    - "deeperlab single shot image parser"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "deep high resolution representation learning for visual recognition"
    - "pifpaf composite fields for human pose estimation"
    - "deep high resolution representation learning for human pose estimation"
    - "panoptic deeplab a simple strong and fast baseline for bottom up panoptic segmentation"
    - "cornernet detecting objects as paired keypoints"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"

Connected through hyperedge HE-2 (induced by source paper "augmented parallel pyramid net for attention guided pose estimation"):
  with members:
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "fast autoaugment"
    - "deep high resolution representation learning for human pose estimation"
    - "scale aware trident networks for object detection"
    - "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"
    - "regularized evolution for image classifier architecture search"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-3 (induced by source paper "memorizing comprehensively to learn adaptively unsupervised cross domain person re id with multi level memory"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "self training with progressive augmentation for unsupervised cross domain person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"

Connected through hyperedge HE-4 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources"):
  with members:
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "godp globally optimized dual pathway deep network architecture for facial landmark localization in the wild"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "learning delicate local representations for multi person pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "res2net a new multi scale backbone architecture"
    - "multi person pose estimation with enhanced channel wise and spatial information"
    - "omni scale feature learning for person re identification"
    - "deep high resolution representation learning for human pose estimation"
    - "quantized densely connected u nets for efficient landmark localization"
    - "learning a discriminative feature network for semantic segmentation"
    - "multi scale structure aware network for human pose estimation"

Connected through hyperedge HE-6 (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-7 (induced by source paper "unsupervised multiple person tracking using autoencoder based lifted multicuts"):
  with members:
    - "tracking without bells and whistles"
    - "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
    - "exploit the connectivity multi object tracking with trackletnet"
    - "unsupervised person re identification by deep learning tracklet association"

Connected through hyperedge HE-8 (induced by source paper "4d association graph for realtime multi person motion capture using multiple video cameras"):
  with members:
    - "fast and robust multi person 3d pose estimation from multiple views"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "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"
    - "simple baselines for human pose estimation and tracking"
    - "crowdpose efficient crowded scenes pose estimation and a new benchmark"
    - "efficient online multi person 2d pose tracking with recurrent spatio temporal affinity fields"
    - "multiposenet fast multi person pose estimation using pose residual network"

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)