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: "extreme clicking for efficient object annotation"
  Abstract: Manually annotating object bounding boxes is central to building computer vision datasets, and it is very time consuming (annotating ILSVRC [53] took 35s for one high-quality box [62]). It involves clicking on imaginary corners of a tight box around the object. This is difficult as these corners are often outside the actual object and several adjustments are required to obtain a tight box. We propose extreme clicking instead: we ask the annotator to click on four physical points on the object: the top, bottom, left- and right-most points. This task is more natural and these points are easy to find. We crowd-source extreme point annotations for PASCAL VOC 2007 and 2012 and show that (1) annotation time is only 7s per box, 5x faster than the traditional way of drawing boxes [62]; (2) the quality of the boxes is as good as the original ground-truth drawn the traditional way; (3) detectors trained on our annotations are as accurate as those trained on the original ground-truth. Moreover, our extreme clicking strategy not only yields box coordinates, but also four accurate boundary points. We show (4) how to incorporate them into GrabCut to obtain more accurate segmentations than those delivered when initializing it from bounding boxes; (5) semantic segmentations models trained on these segmentations outperform those trained on segmentations derived from bounding boxes.

Connected through hyperedge HE-1 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "interactive full image segmentation by considering all regions jointly"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to segment via cut and paste"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual affordance and function understanding a survey"

Connected through hyperedge HE-2 (induced by source paper "active and incremental learning with weak supervision"):
  with members:
    - "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"
    - "yolov3 an incremental improvement"
    - "active learning for deep object detection"
    - "focal loss for dense object detection"
    - "cost effective active learning for deep image classification"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "automatically identifying wild animals in camera trap images with deep learning"
    - "deep bayesian active learning with image data"

Connected through hyperedge HE-3 (induced by source paper "deep snake for real time instance segmentation"):
  with members:
    - "dense reppoints representing visual objects with dense point sets"
    - "mask scoring r cnn"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "objects as points"
    - "fast interactive object annotation with curve gcn"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "bottom up object detection by grouping extreme and center points"
    - "explicit shape encoding for real time instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "reppoints point set representation for object detection"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "region proposal by guided anchoring"
    - "feature selective anchor free module for single shot object detection"
    - "consistent optimization for single shot object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "group normalization"
    - "metaanchor learning to detect objects with customized anchors"

Connected through hyperedge HE-5 (induced by source paper "bottom up object detection by grouping extreme and center points"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "yolov3 an incremental improvement"
    - "simple baselines for human pose estimation and tracking"
    - "acquisition of localization confidence for accurate object detection"
    - "starmap for category agnostic keypoint and viewpoint estimation"
    - "path aggregation network for instance segmentation"
    - "couplenet coupling global structure with local parts for object detection"

Connected through hyperedge HE-6 (induced by source paper "detecting multi oriented text with corner based region proposals"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "deep extreme cut from extreme points to object segmentation"
    - "east an efficient and accurate scene text detector"
    - "oriented response networks"
    - "single shot text detector with regional attention"
    - "fused text segmentation networks for multi oriented scene text detection"
    - "denet scalable real time object detection with directed sparse sampling"

Connected through hyperedge HE-7 (induced by source paper "baod budget aware object detection"):
  with members:
    - "a flexible model for training action localization with varying levels of supervision"
    - "pcn part and context information for pedestrian detection with cnns"
    - "crowd counting by adaptively fusing predictions from an image pyramid"
    - "localization aware active learning for object detection"
    - "towards practical visual search engine within elasticsearch"
    - "generative adversarial active learning"
    - "learning active learning from data"
    - "deep self taught learning for weakly supervised object localization"

Connected through hyperedge HE-8 (induced by source paper "4d generic video object proposals"):
  with members:
    - "mots multi object tracking and segmentation"
    - "bayesian semantic instance segmentation in open set world"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "combined image and world space tracking in traffic scenes"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "unsupervised hard example mining from videos for improved object detection"
    - "maskrnn instance level video object segmentation"

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)