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: "multi person pose estimation with local joint to person associations"
  Abstract: Despite of the recent success of neural networks for human pose estimation, current approaches are limited to pose estimation of a single person and cannot handle humans in groups or crowds. In this work, we propose a method that estimates the poses of multiple persons in an image in which a person can be occluded by another person or might be truncated. To this end, we consider multi-person pose estimation as a joint-to-person association problem. We construct a fully connected graph from a set of detected joint candidates in an image and resolve the joint-to-person association and outlier detection using integer linear programming. Since solving joint-to-person association jointly for all persons in an image is an NP-hard problem and even approximations are expensive, we solve the problem locally for each person. On the challenging MPII Human Pose Dataset for multiple persons, our approach achieves the accuracy of a state-of-the-art method, but it is 6,000 to 19,000 times faster.

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 "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"
    - "pose flow efficient online pose tracking"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "i vise interactive video surveillance as an edge service using unsupervised feature queries"):
  with members:
    - "decentralized smart surveillance through microservices platform"
    - "a lightweight blockchain based privacy protection for smart surveillance at the edge"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "a microservice enabled architecture for smart surveillance using blockchain technology"
    - "kerman a hybrid lightweight tracking algorithm to enable smart surveillance as an edge service"
    - "real time index authentication for event oriented surveillance video query using blockchain"
    - "discovery of shared semantic spaces for multiscene video query and summarization"
    - "compositional human pose regression"

Connected through hyperedge HE-4 (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"
    - "efficient online multi person 2d pose tracking with recurrent spatio temporal affinity fields"

Connected through hyperedge HE-5 (induced by source paper "single stage multi person pose machines"):
  with members:
    - "fast and robust multi person 3d pose estimation from multiple views"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "learning to refine human pose estimation"
    - "single shot multi person 3d pose estimation from monocular rgb"
    - "towards accurate multi person pose estimation in the wild"
    - "pose normalized image generation for person re identification"
    - "joint multi person pose estimation and semantic part segmentation"

Connected through hyperedge HE-6 (induced by source paper "simple pose rethinking and improving a bottom up approach for multi person pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "pifpaf composite fields for human pose estimation"
    - "deep high resolution representation learning for human pose estimation"
    - "cornernet detecting objects as paired keypoints"
    - "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"
    - "focal loss for dense object detection"
    - "squeeze and excitation networks"

Connected through hyperedge HE-7 (induced by source paper "bottom up higher resolution networks for multi person pose estimation"):
  with members:
    - "deeperlab single shot image parser"
    - "deep high resolution representation learning for human pose estimation"
    - "cornernet detecting objects as paired keypoints"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "simple baselines for human pose estimation and tracking"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"

Connected through hyperedge HE-8 (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"
    - "efficient online multi person 2d pose tracking with recurrent spatio temporal affinity fields"
    - "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"

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)