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: "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
  Abstract: We present a box-free bottom-up approach for the tasks of pose estimation and instance segmentation of people in multi-person images using an efficient single-shot model. The proposed PersonLab model tackles both semantic-level reasoning and object-part associations using part-based modeling. Our model employs a convolutional network which learns to detect individual keypoints and predict their relative displacements, allowing us to group keypoints into person pose instances. Further, we propose a part-induced geometric embedding descriptor which allows us to associate semantic person pixels with their corresponding person instance, delivering instance-level person segmentations. Our system is based on a fully-convolutional architecture and allows for efficient inference, with runtime essentially independent of the number of people present in the scene. Trained on COCO data alone, our system achieves COCO test-dev keypoint average precision of 0.665 using single-scale inference and 0.687 using multi-scale inference, significantly outperforming all previous bottom-up pose estimation systems. We are also the first bottom-up method to report competitive results for the person class in the COCO instance segmentation task, achieving a person category average precision of 0.417.

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"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-2 (induced by source paper "deep affinity net instance segmentation via affinity"):
  with members:
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "upsnet a unified panoptic segmentation network"
    - "deeperlab single shot image parser"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "panoptic feature pyramid networks"
    - "cornernet detecting objects as paired keypoints"
    - "instance segmentation by deep coloring"
    - "bshapenet object detection and instance segmentation with bounding shape masks"

Connected through hyperedge HE-3 (induced by source paper "augmented parallel pyramid net for attention guided pose estimation"):
  with members:
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "fast autoaugment"
    - "deep high resolution representation learning for human pose estimation"
    - "scale aware trident networks for object detection"
    - "multi scale structure aware network for human pose estimation"
    - "regularized evolution for image classifier architecture search"
    - "simple baselines for human pose estimation and tracking"
    - "detnet a backbone network for object detection"

Connected through hyperedge HE-4 (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"
    - "multi scale structure aware network for human pose estimation"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-5 (induced by source paper "towards high performance human keypoint detection"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "ordinal depth supervision for 3d human pose estimation"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "deep learning for generic object detection a survey"
    - "simple baselines for human pose estimation and tracking"
    - "3d human pose estimation in the wild by adversarial learning"
    - "glimpse clouds human activity recognition from unstructured feature points"

Connected through hyperedge HE-6 (induced by source paper "4d association graph for realtime multi person motion capture using multiple video cameras"):
  with members:
    - "fast and robust multi person 3d pose estimation from multiple views"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "multi scale structure aware network for human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "crowdpose efficient crowded scenes pose estimation and a new benchmark"
    - "efficient online multi person 2d pose tracking with recurrent spatio temporal affinity fields"
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "end to end learning for graph decomposition"

Connected through hyperedge HE-7 (induced by source paper "sideinfnet a deep neural network for semi automatic semantic segmentation with side information"):
  with members:
    - "instance embedding transfer to unsupervised video object segmentation"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep grabcut for object selection"
    - "a unified model for near and remote sensing"
    - "convolutional networks with adaptive inference graphs"
    - "pose2instance harnessing keypoints for person instance segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-8 (induced by source paper "image based ood detector principles on graph based input data in human action recognition"):
  with members:
    - "human action recognition and prediction a survey"
    - "metric learning for novelty and anomaly detection"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "novelty detection with gan"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "stacked hourglass networks for human pose estimation"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "a baseline for detecting misclassified and out of distribution examples in 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)