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: "posefix model agnostic general human pose refinement network"
  Abstract: Multi-person pose estimation from a 2D image is an essential technique for human behavior understanding. In this paper, we propose a human pose refinement network that estimates a refined pose from a tuple of an input image and input pose. The pose refinement was performed mainly through an end-to-end trainable multi-stage architecture in previous methods. However, they are highly dependent on pose estimation models and require careful model design. By contrast, we propose a model-agnostic pose refinement method. According to a recent study, state-of-the-art 2D human pose estimation methods have similar error distributions. We use this error statistics as prior information to generate synthetic poses and use the synthesized poses to train our model. In the testing stage, pose estimation results of any other methods can be input to the proposed method. Moreover, the proposed model does not require code or knowledge about other methods, which allows it to be easily used in the post-processing step. We show that the proposed approach achieves better performance than the conventional multi-stage refinement models and consistently improves the performance of various state-of-the-art pose estimation methods on the commonly used benchmark. The code is available in this https URL\footnote{\url{this https URL}}.

Connected through hyperedge HE-1 (induced by source paper "patch based 3d human pose refinement"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "monocular 3d human pose estimation by generation and ordinal ranking"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "ordinal depth supervision for 3d human pose estimation"
    - "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
    - "task oriented hand motion retargeting for dexterous manipulation imitation"
    - "bodynet volumetric inference of 3d human body shapes"
    - "learning to refine human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "camera distance aware top down approach for 3d multi person pose estimation from a single rgb image"):
  with members:
    - "multi scale aggregation r cnn for 2d multi person pose estimation"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "deep attention based classification network for robust depth prediction"
    - "deep ordinal regression network for monocular depth estimation"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "simple baselines for human pose estimation and tracking"
    - "3d human pose estimation in the wild by adversarial learning"
    - "multiposenet fast multi person pose estimation using pose residual network"

Connected through hyperedge HE-3 (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-4 (induced by source paper "multi scale aggregation r cnn for 2d multi person pose estimation"):
  with members:
    - "simple baselines for human pose estimation and tracking"
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "megdet a large mini batch object detector"
    - "towards accurate multi person pose estimation in the wild"
    - "cascaded pyramid network for multi person pose estimation"
    - "multi context attention for human pose estimation"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-5 (induced by source paper "absposelifter absolute 3d human pose lifting network from a single noisy 2d human pose"):
  with members:
    - "simple baselines for human pose estimation and tracking"
    - "3d human pose estimation in the wild by adversarial learning"
    - "numerical coordinate regression with convolutional neural networks"
    - "synthetic occlusion augmentation with volumetric heatmaps for the 2018 eccv posetrack challenge on 3d human pose estimation"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

Connected through hyperedge HE-6 (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"
    - "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"
    - "2d 3d pose estimation and action recognition using multitask deep learning"

Connected through hyperedge HE-7 (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"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "joint flow temporal flow fields for multi person tracking"
    - "multi scale structure aware network for human pose estimation"

Connected through hyperedge HE-8 (induced by source paper "towards good practices for multi person pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "multi person pose estimation with enhanced channel wise and spatial information"
    - "associative embedding end to end learning for joint detection and grouping"
    - "deep residual learning for image recognition"

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)