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: "fast human pose estimation"
  Abstract: Existing human pose estimation approaches often only consider how to improve the model generalisation performance, but putting aside the significant efficiency problem. This leads to the development of heavy models with poor scalability and cost-effectiveness in practical use. In this work, we investigate the under-studied but practically critical pose model efficiency problem. To this end, we present a new Fast Pose Distillation (FPD) model learning strategy. Specifically, the FPD trains a lightweight pose neural network architecture capable of executing rapidly with low computational cost. It is achieved by effectively transferring the pose structure knowledge of a strong teacher network. Extensive evaluations demonstrate the advantages of our FPD method over a broad range of state-of-the-art pose estimation approaches in terms of model cost-effectiveness on two standard benchmark datasets, MPII Human Pose and Leeds Sports Pose.

Connected through hyperedge HE-1 (induced by source paper "lightweight 3d human pose estimation network training using teacher student learning"):
  with members:
    - "learnable triangulation of human pose"
    - "improved training of binary networks for human pose estimation and image recognition"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "xnect real time multi person 3d human pose estimation with a single rgb camera"
    - "deep high resolution representation learning for human pose estimation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "ordinal depth supervision for 3d human pose estimation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "attention a lightweight 2d hand pose estimation approach"):
  with members:
    - "real time facial surface geometry from monocular video on mobile gpus"
    - "making convolutional networks shift invariant again"
    - "freihand a dataset for markerless capture of hand pose and shape from single rgb images"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "3d hand shape and pose from images in the wild"
    - "attention augmented convolutional networks"
    - "mish a self regularized non monotonic neural activation function"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-3 (induced by source paper "repose learning deep kinematic priors for fast human pose estimation"):
  with members:
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "multi context attention for human pose estimation"
    - "stacked hourglass networks for human pose estimation"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "structured feature learning for pose estimation"
    - "deepercut a deeper stronger and faster multi person pose estimation model"

Connected through hyperedge HE-4 (induced by source paper "multi stage hrnet multiple stage high resolution network for human pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "human following for wheeled robot with monocular pan tilt camera"
    - "deep high resolution representation learning for human pose estimation"
    - "hybrid task cascade for instance segmentation"
    - "exploiting offset guided network for pose estimation and tracking"
    - "state aware re identification feature for multi target multi camera tracking"
    - "fastpose towards real time pose estimation and tracking via scale normalized multi task networks"
    - "action machine rethinking action recognition in trimmed videos"

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 "global context for convolutional pose machines"):
  with members:
    - "human pose estimation with spatial contextual information"
    - "real time 2d multi person pose estimation on cpu lightweight openpose"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-7 (induced by source paper "kptransfer improved performance and faster convergence from keypoint subset wise domain transfer in human pose estimation"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "multi scale structure aware network for human pose estimation"
    - "3d human pose estimation in the wild by adversarial learning"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "learning feature pyramids for human pose estimation"
    - "cascaded pyramid network for multi person pose estimation"
    - "convolutional pose machines"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised semantic image segmentation with self correcting networks"):
  with members:
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "weakly supervised instance segmentation using class peak response"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "joint optimization framework for learning with noisy labels"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"

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)