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 for action action for pose"
  Abstract: In this work we propose to utilize information about human actions to improve pose estimation in monocular videos. To this end, we present a pictorial structure model that exploits high-level information about activities to incorporate higher-order part dependencies by modeling action specific appearance models and pose priors. However, instead of using an additional expensive action recognition framework, the action priors are efficiently estimated by our pose estimation framework. This is achieved by starting with a uniform action prior and updating the action prior during pose estimation. We also show that learning the right amount of appearance sharing among action classes improves the pose estimation. Our proposed model achieves state-of-the-art performance on two challenging datasets for pose estimation and action recognition with over 80,000 test images.

Connected through hyperedge HE-1 (induced by source paper "unipose unified human pose estimation in single images and videos"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "cascade feature aggregation for human pose estimation"
    - "human pose estimation with spatial contextual information"
    - "deep high resolution representation learning for human pose estimation"
    - "lighttrack a generic framework for online top down human pose tracking"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "multi task deep learning for real time 3d human pose estimation and action recognition"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "lifting from the deep convolutional 3d pose estimation from a single image"

Connected through hyperedge HE-3 (induced by source paper "mimetics towards understanding human actions out of context"):
  with members:
    - "repair removing representation bias by dataset resampling"
    - "star net action recognition using spatio temporal activation reprojection"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "action machine rethinking action recognition in trimmed videos"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "pose based two stream relational networks for action recognition in videos"

Connected through hyperedge HE-4 (induced by source paper "self supervised learning of class embeddings from video"):
  with members:
    - "learning landmarks from unaligned data using image translation"
    - "growing a brain fine tuning by increasing model capacity"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "self supervised learning of a facial attribute embedding from video"
    - "self supervised spatiotemporal feature learning via video rotation prediction"
    - "unsupervised discovery of object landmarks as structural representations"
    - "a fully progressive approach to single image super resolution"
    - "voxceleb2 deep speaker recognition"

Connected through hyperedge HE-5 (induced by source paper "action recognition via pose based graph convolutional networks with intermediate dense supervision"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "videos as space time region graphs"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-6 (induced by source paper "dynamic kernel distillation for efficient pose estimation in videos"):
  with members:
    - "simple baselines for human pose estimation and tracking"
    - "began boundary equilibrium generative adversarial networks"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "end to end representation learning for correlation filter based tracking"
    - "lstm pose machines"
    - "self adversarial training for human pose estimation"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "detect and track efficient pose estimation in videos"

Connected through hyperedge HE-7 (induced by source paper "multi modal three stream network for action recognition"):
  with members:
    - "pose conditioned spatio temporal attention for human action recognition"
    - "convolutional pose machines"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "a hierarchical pose based approach to complex action understanding using dictionaries of actionlets and motion poselets"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "p cnn pose based cnn features for action recognition"

Connected through hyperedge HE-8 (induced by source paper "large area 3d human pose detection via stereo reconstruction in panoramic cameras"):
  with members:
    - "dual path networks for multi person human pose estimation"
    - "simple online and realtime tracking with a deep association metric"
    - "posetrack joint multi person pose estimation and tracking"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "flowing convnets for human pose estimation in videos"
    - "very deep convolutional networks for large scale image recognition"
    - "deeppose human pose estimation via deep neural 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)