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: "joint flow temporal flow fields for multi person tracking"
  Abstract: In this work we propose an online multi person pose tracker which works on two consecutive frames $I_{t-1}$ and $I_t$. The general formulation of our temporal network allows to rely on any multi person pose estimation network as spatial network. From the spatial network we extract image features and pose features for both frames. These features compose as input for our temporal model that predicts Temporal Flow Fields (TFF). These TFF are vector fields which indicate the direction in which body joint is going to move from frame $I_{t-1}$ to frame $I_t$. This novel representation allows to formulate a similarity measure of detected joints. These similarities are used as binary potentials in an bipartite graph optimization problem in order to perform tracking of multiple poses. We show that these TFF can be learned by a relative small CNN network whilst achieving state-of-the-art multi person pose tracking results.

Connected through hyperedge HE-1 (induced by source paper "fastpose towards real time pose estimation and tracking via scale normalized multi task networks"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "exploiting offset guided network for pose estimation and tracking"
    - "pose flow efficient online pose tracking"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "distractor aware siamese networks for visual object tracking"
    - "attention guided unified network for panoptic segmentation"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-2 (induced by source paper "multi person articulated tracking with spatial and temporal embeddings"):
  with members:
    - "pose flow efficient online pose tracking"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "simple baselines for human pose estimation and tracking"
    - "the devil is in the middle exploiting mid level representations for cross domain instance matching"
    - "recurrent pixel embedding for instance grouping"
    - "generative partition networks for multi person pose estimation"
    - "towards accurate multi person pose estimation in the wild"
    - "posetrack a benchmark for human pose estimation and tracking"

Connected through hyperedge HE-3 (induced by source paper "exploiting offset guided network for pose estimation and tracking"):
  with members:
    - "pose flow efficient online pose tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "simple baselines for human pose estimation and tracking"
    - "multi hierarchical independent correlation filters for visual tracking"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "learning feature pyramids for human pose estimation"
    - "uct learning unified convolutional networks for real time visual tracking"

Connected through hyperedge HE-4 (induced by source paper "pose estimator and tracker using temporal flow maps for limbs"):
  with members:
    - "pose flow efficient online pose tracking"
    - "simple baselines for human pose estimation and tracking"
    - "learning feature pyramids for human pose estimation"
    - "dual path networks for multi person human pose estimation"
    - "towards accurate multi person pose estimation in the wild"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "posetrack a benchmark for human pose estimation and tracking"
    - "multi context attention for human pose estimation"

Connected through hyperedge HE-5 (induced by source paper "lighttrack a generic framework for online top down human pose tracking"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "online multi object tracking with dual matching attention networks"
    - "deep high resolution representation learning for human pose estimation"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "posefix model agnostic general human pose refinement network"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "multi scale structure aware network for human pose estimation"

Connected through hyperedge HE-6 (induced by source paper "single camera basketball tracker through pose and semantic feature fusion"):
  with members:
    - "learning correspondence from the cycle consistency of time"
    - "unsupervised deep tracking"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "fusion of head and full body detectors for multi object tracking"
    - "convolutional pose machines"
    - "posetrack joint multi person pose estimation and tracking"
    - "arttrack articulated multi person tracking in the wild"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-7 (induced by source paper "multi person tracking by multi scale detection in basketball scenarios"):
  with members:
    - "fusion of head and full body detectors for multi object tracking"
    - "detect and track efficient pose estimation in videos"
    - "mask r cnn"
    - "convolutional pose machines"
    - "posetrack joint multi person pose estimation and tracking"
    - "arttrack articulated multi person tracking in the wild"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-8 (induced by source paper "efficient online multi person 2d pose tracking with recurrent spatio temporal affinity fields"):
  with members:
    - "multi domain pose network for multi person pose estimation and tracking"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "pose flow efficient online pose tracking"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "simple baselines for human pose estimation and tracking"
    - "densepose dense human pose estimation in the wild"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "lstm pose machines"

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)