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: "polarmask single shot instance segmentation with polar representation"
  Abstract: In this paper, we introduce an anchor-box free and single shot instance segmentation method, which is conceptually simple, fully convolutional and can be used as a mask prediction module for instance segmentation, by easily embedding it into most off-the-shelf detection methods. Our method, termed PolarMask, formulates the instance segmentation problem as instance center classification and dense distance regression in a polar coordinate. Moreover, we propose two effective approaches to deal with sampling high-quality center examples and optimization for dense distance regression, respectively, which can significantly improve the performance and simplify the training process. Without any bells and whistles, PolarMask achieves 32.9% in mask mAP with single-model and single-scale training/testing on challenging COCO dataset. For the first time, we demonstrate a much simpler and flexible instance segmentation framework achieving competitive accuracy. We hope that the proposed PolarMask framework can serve as a fundamental and strong baseline for single shot instance segmentation tasks. Code is available at: this http URL.

Connected through hyperedge HE-1 (induced by source paper "pointins point based instance segmentation"):
  with members:
    - "deep snake for real time instance segmentation"
    - "embedmask embedding coupling for one stage instance segmentation"
    - "mask scoring r cnn"
    - "tensormask a foundation for dense object segmentation"
    - "hybrid task cascade for instance segmentation"
    - "fcos fully convolutional one stage object detection"
    - "yolact real time instance segmentation"
    - "solo segmenting objects by locations"

Connected through hyperedge HE-2 (induced by source paper "blendmask top down meets bottom up for instance segmentation"):
  with members:
    - "pointrend image segmentation as rendering"
    - "lvis a dataset for large vocabulary instance segmentation"
    - "yolact better real time instance segmentation"
    - "imp instance mask projection for high accuracy semantic segmentation of things"
    - "tensormask a foundation for dense object segmentation"
    - "fcos fully convolutional one stage object detection"
    - "yolact real time instance segmentation"
    - "solo segmenting objects by locations"

Connected through hyperedge HE-3 (induced by source paper "fouriernet compact mask representation for instance segmentation using differentiable shape decoders"):
  with members:
    - "embedmask embedding coupling for one stage instance segmentation"
    - "dense reppoints representing visual objects with dense point sets"
    - "polytransform deep polygon transformer for instance segmentation"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "mask scoring r cnn"
    - "hybrid task cascade for instance segmentation"
    - "fcos fully convolutional one stage object detection"
    - "yolact real time instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "shape aware feature extraction for instance segmentation"):
  with members:
    - "mask scoring r cnn"
    - "tensormask a foundation for dense object segmentation"
    - "yolact real time instance segmentation"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "bottom up object detection by grouping extreme and center points"
    - "path aggregation network for instance segmentation"
    - "segmentation aware convolutional networks using local attention masks"
    - "pixelwise instance segmentation with a dynamically instantiated network"

Connected through hyperedge HE-5 (induced by source paper "dense reppoints representing visual objects with dense point sets"):
  with members:
    - "darnet deep active ray network for building segmentation"
    - "reppoints point set representation for object detection"
    - "pointrend image segmentation as rendering"
    - "centernet keypoint triplets for object detection"
    - "tensormask a foundation for dense object segmentation"
    - "yolact real time instance segmentation"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"

Connected through hyperedge HE-6 (induced by source paper "real time panoptic segmentation from dense detections"):
  with members:
    - "panoptic segmentation"
    - "an end to end network for panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "fast panoptic segmentation network"
    - "adaptis adaptive instance selection network"
    - "seamless scene segmentation"
    - "deeperlab single shot image parser"
    - "mask scoring r cnn"

Connected through hyperedge HE-7 (induced by source paper "embedmask embedding coupling for one stage instance segmentation"):
  with members:
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "mask scoring r cnn"
    - "tensormask a foundation for dense object segmentation"
    - "fcos fully convolutional one stage object detection"
    - "yolact real time instance segmentation"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "path aggregation network for instance segmentation"
    - "focal loss for dense object detection"

Connected through hyperedge HE-8 (induced by source paper "iou aware single stage object detector for accurate localization"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "revisiting feature alignment for one stage object detection"
    - "iou balanced loss functions for single stage object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "fcos fully convolutional one stage object detection"
    - "prime sample attention in object detection"
    - "yolov3 an incremental improvement"
    - "gradient harmonized single stage detector"

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)