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: "efficient online multi person 2d pose tracking with recurrent spatio temporal affinity fields"
  Abstract: We present an online approach to efficiently and simultaneously detect and track the 2D pose of multiple people in a video sequence. We build upon Part Affinity Field (PAF) representation designed for static images, and propose an architecture that can encode and predict Spatio-Temporal Affinity Fields (STAF) across a video sequence. In particular, we propose a novel temporal topology cross-linked across limbs which can consistently handle body motions of a wide range of magnitudes. Additionally, we make the overall approach recurrent in nature, where the network ingests STAF heatmaps from previous frames and estimates those for the current frame. Our approach uses only online inference and tracking, and is currently the fastest and the most accurate bottom-up approach that is runtime invariant to the number of people in the scene and accuracy invariant to input frame rate of camera. Running at $\sim$30 fps on a single GPU at single scale, it achieves highly competitive results on the PoseTrack benchmarks.

Connected through hyperedge HE-1 (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"
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "end to end learning for graph decomposition"

Connected through hyperedge HE-2 (induced by source paper "15 keypoints is all you need"):
  with members:
    - "spatial temporal relation networks for multi object tracking"
    - "lxmert learning cross modality encoder representations from transformers"
    - "learning video representations using contrastive bidirectional transformer"
    - "learning temporal pose estimation from sparsely labeled videos"
    - "stand alone self attention in vision models"
    - "learning correspondence from the cycle consistency of time"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "mmdetection open mmlab detection toolbox and benchmark"

Connected through hyperedge HE-3 (induced by source paper "single network whole body pose estimation"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "recycle gan unsupervised video retargeting"
    - "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"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "simple baselines for human pose estimation and tracking"
    - "monocular total capture posing face body and hands in the wild"
    - "hand pose estimation via latent 2 5d heatmap regression"

Connected through hyperedge HE-4 (induced by source paper "person in wifi fine grained person perception using wifi"):
  with members:
    - "joint activity recognition and indoor localization with wifi fingerprints"
    - "efficient continuous time slam for 3d lidar based online mapping"
    - "csi net unified human body characterization and action recognition"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "simple baselines for human pose estimation and tracking"
    - "characterization of a rs lidar for 3d perception"
    - "combining lidar space clustering and convolutional neural networks for pedestrian detection"
    - "towards accurate multi person pose estimation in the wild"

Connected through hyperedge HE-5 (induced by source paper "followmeup sports new benchmark for 2d human keypoint recognition"):
  with members:
    - "multi person pose estimation with enhanced channel wise and spatial information"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "pifpaf composite fields for human pose estimation"
    - "deep high resolution representation learning for human pose estimation"
    - "posefix model agnostic general human pose refinement network"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "real time 2d multi person pose estimation on cpu lightweight openpose"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-6 (induced by source paper "multi person pose tracking using sequential monte carlo with probabilistic neural pose predictor"):
  with members:
    - "pose estimator and tracker using temporal flow maps for limbs"
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "uncertainty aware imitation learning using kernelized movement primitives"
    - "uncertainty aware learning from demonstrations in multiple contexts using bayesian neural networks"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "multi domain pose network for multi person pose estimation and tracking"
    - "pose flow efficient online pose tracking"
    - "a fusion approach for multi frame optical flow estimation"

Connected through hyperedge HE-7 (induced by source paper "unifying part detection and association for recurrent multi person pose estimation"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "simple baselines for human pose estimation and tracking"
    - "towards accurate multi person pose estimation in the wild"
    - "recurrent neural networks for semantic instance segmentation"
    - "stacked hourglass networks for human pose estimation"
    - "rmpe regional multi person pose estimation"
    - "posetrack joint multi person pose estimation and tracking"

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)