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: "prime sample attention in object detection"
  Abstract: It is a common paradigm in object detection frameworks to treat all samples equally and target at maximizing the performance on average. In this work, we revisit this paradigm through a careful study on how different samples contribute to the overall performance measured in terms of mAP. Our study suggests that the samples in each mini-batch are neither independent nor equally important, and therefore a better classifier on average does not necessarily mean higher mAP. Motivated by this study, we propose the notion of Prime Samples, those that play a key role in driving the detection performance. We further develop a simple yet effective sampling and learning strategy called PrIme Sample Attention (PISA) that directs the focus of the training process towards such samples. Our experiments demonstrate that it is often more effective to focus on prime samples than hard samples when training a detector. Particularly, On the MSCOCO dataset, PISA outperforms the random sampling baseline and hard mining schemes, e.g. OHEM and Focal Loss, consistently by more than 1% on both single-stage and two-stage detectors, with a strong backbone ResNeXt-101.

Connected through hyperedge HE-1 (induced by source paper "learning from noisy anchors for one stage object detection"):
  with members:
    - "reppoints point set representation for object detection"
    - "cascade retinanet maintaining consistency for single stage object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "centernet keypoint triplets for object detection"
    - "region proposal by guided anchoring"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"

Connected through hyperedge HE-2 (induced by source paper "iou uniform r cnn breaking through the limitations of rpn"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "centernet keypoint triplets for object detection"
    - "imbalance problems in object detection a review"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "cornernet detecting objects as paired keypoints"
    - "bounding box regression with uncertainty for accurate object detection"

Connected through hyperedge HE-3 (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"
    - "polarmask single shot instance segmentation with polar representation"
    - "iou balanced loss functions for single stage object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "fcos fully convolutional one stage object detection"
    - "yolov3 an incremental improvement"
    - "gradient harmonized single stage detector"

Connected through hyperedge HE-4 (induced by source paper "are sampling heuristics necessary in object detectors"):
  with members:
    - "reppoints point set representation for object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "imbalance problems in object detection a review"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "bottom up object detection by grouping extreme and center points"

Connected through hyperedge HE-5 (induced by source paper "generating positive bounding boxes for balanced training of object detectors"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "imbalance problems in object detection a review"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "deep feature pyramid reconfiguration for object detection"
    - "path aggregation network for instance segmentation"
    - "localization recall precision lrp a new performance metric for object detection"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "focal loss for dense object detection"

Connected through hyperedge HE-6 (induced by source paper "is sampling heuristics necessary in training deep object detectors"):
  with members:
    - "reppoints point set representation for object detection"
    - "region proposal by guided anchoring"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "cornernet detecting objects as paired keypoints"
    - "yolov3 an incremental improvement"
    - "gradient harmonized single stage detector"
    - "focal loss for dense object detection"

Connected through hyperedge HE-7 (induced by source paper "iou balanced loss functions for single stage object detection"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "mask scoring r cnn"
    - "gradient harmonized single stage detector"
    - "acquisition of localization confidence for accurate object detection"
    - "soft nms improving object detection with one line of code"
    - "improving object localization with fitness nms and bounded iou loss"
    - "focal loss for dense object detection"

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)