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: "pose flow efficient online pose tracking"
  Abstract: Multi-person articulated pose tracking in complex unconstrained videos is an important and challenging problem. In this paper, going along the road of top-down approaches, we propose a decent and efficient pose tracker based on pose flows. First, we design an online optimization framework to build association of cross-frame poses and form pose flows. Second, a novel pose flow non maximum suppression (NMS) is designed to robustly reduce redundant pose flows and re-link temporal disjoint pose flows. Extensive experiments show our method significantly outperforms best reported results on two standard Pose Tracking datasets (PoseTrack dataset and PoseTrack Challenge dataset) by 13 mAP 25 MOTA and 6 mAP 3 MOTA respectively. Moreover, in the case of working on detected poses in individual frames, the extra computation of proposed pose tracker is very minor, requiring 0.01 second per frame only.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "disentangling human dynamics for pedestrian locomotion forecasting with noisy supervision"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "is the pedestrian going to cross answering by 2d pose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "scene graph generation via conditional random fields"

Connected through hyperedge HE-2 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition"):
  with members:
    - "learning class regularized features for action recognition"
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "real world anomaly detection in surveillance videos"
    - "sports camera calibration via synthetic data"
    - "memory attention networks for skeleton based action recognition"

Connected through hyperedge HE-3 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "transgaga geometry aware unsupervised image to image translation"
    - "emergence of object segmentation in perturbed generative models"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "learning conditional deformable templates with convolutional networks"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "voxelmorph a learning framework for deformable medical image registration"

Connected through hyperedge HE-4 (induced by source paper "comprehensive and efficient data labeling via adaptive model scheduling"):
  with members:
    - "a survey on multi output learning"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "yolov3 an incremental improvement"
    - "improved deep hashing with soft pairwise similarity for multi label image retrieval"
    - "instance similarity deep hashing for multi label image retrieval"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "finehand learning hand shapes for american sign language recognition"):
  with members:
    - "sign language recognition analysis using multimodal data"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "towards accurate multi person pose estimation in the wild"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "skepxels spatio temporal image representation of human skeleton joints for action recognition"
    - "convolutional pose machines"

Connected through hyperedge HE-6 (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"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "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-7 (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"
    - "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"
    - "human pose estimation using global and local normalization"

Connected through hyperedge HE-8 (induced by source paper "learning temporal pose estimation from sparsely labeled videos"):
  with members:
    - "learning correspondence from the cycle consistency of time"
    - "deep high resolution representation learning for human pose estimation"
    - "multi domain pose network for multi person pose estimation and tracking"
    - "self supervised learning of a facial attribute embedding from video"
    - "simple baselines for human pose estimation and tracking"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "focal loss for dense object detection"
    - "deformable convolutional 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)