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: "single shot refinement neural network for object detection"
  Abstract: For object detection, the two-stage approach (e.g., Faster R-CNN) has been achieving the highest accuracy, whereas the one-stage approach (e.g., SSD) has the advantage of high efficiency. To inherit the merits of both while overcoming their disadvantages, in this paper, we propose a novel single-shot based detector, called RefineDet, that achieves better accuracy than two-stage methods and maintains comparable efficiency of one-stage methods. RefineDet consists of two inter-connected modules, namely, the anchor refinement module and the object detection module. Specifically, the former aims to (1) filter out negative anchors to reduce search space for the classifier, and (2) coarsely adjust the locations and sizes of anchors to provide better initialization for the subsequent regressor. The latter module takes the refined anchors as the input from the former to further improve the regression and predict multi-class label. Meanwhile, we design a transfer connection block to transfer the features in the anchor refinement module to predict locations, sizes and class labels of objects in the object detection module. The multi-task loss function enables us to train the whole network in an end-to-end way. Extensive experiments on PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO demonstrate that RefineDet achieves state-of-the-art detection accuracy with high efficiency. Code is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "revisiting the sibling head in object detector"):
  with members:
    - "reppoints point set representation for object detection"
    - "double head rcnn rethinking classification and localization for object detection"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "centernet keypoint triplets for object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-2 (induced by source paper "joint anchor feature refinement for real time accurate object detection in images and videos"):
  with members:
    - "cascade retinanet maintaining consistency for single stage object detection"
    - "region proposal by guided anchoring"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "object detection in video with spatiotemporal sampling networks"
    - "towards high performance video object detection for mobiles"

Connected through hyperedge HE-3 (induced by source paper "cpm r cnn calibrating point guided misalignment in object detection"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "grid r cnn plus faster and better"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "mask scoring r cnn"
    - "fcos fully convolutional one stage object detection"
    - "cornernet detecting objects as paired keypoints"
    - "grid r cnn"
    - "revisiting rcnn on awakening the classification power of faster rcnn"

Connected through hyperedge HE-4 (induced by source paper "penet object detection using points estimation in aerial images"):
  with members:
    - "clustered object detection in aerial images"
    - "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"
    - "objects as points"
    - "augmentation for small object detection"
    - "vision meets drones a challenge"
    - "cornernet detecting objects as paired keypoints"
    - "yolo lite a real time object detection algorithm optimized for non gpu computers"
    - "detnet a backbone network for object detection"

Connected through hyperedge HE-5 (induced by source paper "rethinking object detection in retail stores"):
  with members:
    - "reppoints point set representation for object detection"
    - "data priming network for automatic check out"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "rpc a large scale retail product checkout dataset"
    - "object counting and instance segmentation with image level supervision"
    - "cornernet detecting objects as paired keypoints"
    - "mvtec d2s densely segmented supermarket dataset"

Connected through hyperedge HE-6 (induced by source paper "rethinking temporal object detection from robotic perspectives"):
  with members:
    - "joint anchor feature refinement for real time accurate object detection in images and videos"
    - "siamfc towards robust and accurate visual tracking with target estimation guidelines"
    - "atom accurate tracking by overlap maximization"
    - "object detection in video with spatiotemporal sampling networks"
    - "temporally identity aware ssd with attentional lstm"
    - "towards high performance video object detection"
    - "focal loss for dense object detection"
    - "detect to track and track to detect"

Connected through hyperedge HE-7 (induced by source paper "reveal of domain effect how visual restoration contributes to object detection in aquatic scenes"):
  with members:
    - "real world underwater enhancement challenges benchmarks and solutions"
    - "a robust learning approach to domain adaptive object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "selective refinement network for high performance face detection"
    - "focal loss for dense object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-8 (induced by source paper "bridging the gap between anchor based and anchor free detection via adaptive training sample selection"):
  with members:
    - "reppoints point set representation for object detection"
    - "foveabox beyond anchor based object detector"
    - "libra r cnn towards balanced learning for object detection"
    - "centernet keypoint triplets for object detection"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "region proposal by guided anchoring"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "towards accurate one stage object detection with ap loss"

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)