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: "rethinking on multi stage networks for human pose estimation"
  Abstract: Existing pose estimation approaches fall into two categories: single-stage and multi-stage methods. While multi-stage methods are seemingly more suited for the task, their performance in current practice is not as good as single-stage methods. This work studies this issue. We argue that the current multi-stage methods' unsatisfactory performance comes from the insufficiency in various design choices. We propose several improvements, including the single-stage module design, cross stage feature aggregation, and coarse-to-fine supervision. The resulting method establishes the new state-of-the-art on both MS COCO and MPII Human Pose dataset, justifying the effectiveness of a multi-stage architecture. The source code is publicly available for further research.

Connected through hyperedge HE-1 (induced by source paper "learning delicate local representations for multi person pose estimation"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "multi person pose estimation with enhanced channel wise and spatial information"
    - "omni scale feature learning for person re identification"
    - "deep high resolution representation learning for human pose estimation"
    - "quantized densely connected u nets for efficient landmark localization"
    - "learning a discriminative feature network for semantic segmentation"
    - "multi scale structure aware network for human pose estimation"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-2 (induced by source paper "spgnet semantic prediction guidance for scene parsing"):
  with members:
    - "seamless scene segmentation"
    - "deeperlab single shot image parser"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "multi scale structure aware network for human pose estimation"

Connected through hyperedge HE-3 (induced by source paper "mixture dense regression for object detection and human pose estimation"):
  with members:
    - "lvis a dataset for large vocabulary instance segmentation"
    - "overcoming limitations of mixture density networks a sampling and fitting framework for multimodal future prediction"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "feature selective anchor free module for single shot object detection"
    - "unsupervised discovery of parts structure and dynamics"
    - "pifpaf composite fields for human pose estimation"
    - "scale aware trident networks for object detection"
    - "objects as points"

Connected through hyperedge HE-4 (induced by source paper "cascade feature aggregation for human pose estimation"):
  with members:
    - "human pose estimation with spatial contextual information"
    - "quantized densely connected u nets for efficient landmark localization"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "multi scale structure aware network for human pose estimation"
    - "learning to refine human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "learning feature pyramids for human pose estimation"
    - "human pose regression by combining indirect part detection and contextual information"

Connected through hyperedge HE-5 (induced by source paper "camera to robot pose estimation from a single image"):
  with members:
    - "dpod 6d pose object detector and refiner"
    - "real time 3d traffic cone detection for autonomous driving"
    - "deep high resolution representation learning for human pose estimation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "real time highly accurate robotic grasp detection using fully convolutional neural networks with high resolution images"
    - "simple baselines for human pose estimation and tracking"
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"

Connected through hyperedge HE-6 (induced by source paper "multi stage hrnet multiple stage high resolution network for human pose estimation"):
  with members:
    - "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"
    - "distractor aware siamese networks for visual object tracking"

Connected through hyperedge HE-7 (induced by source paper "trb a novel triplet representation for understanding 2d human body"):
  with members:
    - "human pose estimation with spatial contextual information"
    - "multi person articulated tracking with spatial and temporal embeddings"
    - "simple baselines for human pose estimation and tracking"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "dense pose transfer"
    - "densepose dense human pose estimation in the wild"
    - "detecting and recognizing human object interactions"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"

Connected through hyperedge HE-8 (induced by source paper "simple and lightweight human pose estimation"):
  with members:
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "deep high resolution representation learning for human pose estimation"
    - "multi scale structure aware network for human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "non local neural networks"
    - "learning feature pyramids for human pose estimation"
    - "squeeze and excitation networks"
    - "human pose regression by combining indirect part detection and contextual information"

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)