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: "end to end training of object class detectors for mean average precision"
  Abstract: We present a method for training CNN-based object class detectors directly using mean average precision (mAP) as the training loss, in a truly end-to-end fashion that includes non-maximum suppression (NMS) at training time. This contrasts with the traditional approach of training a CNN for a window classification loss, then applying NMS only at test time, when mAP is used as the evaluation metric in place of classification accuracy. However, mAP following NMS forms a piecewise-constant structured loss over thousands of windows, with gradients that do not convey useful information for gradient descent. Hence, we define new, general gradient-like quantities for piecewise constant functions, which have wide applicability. We describe how to calculate these efficiently for mAP following NMS, enabling to train a detector based on Fast R-CNN directly for mAP. This model achieves equivalent performance to the standard Fast R-CNN on the PASCAL VOC 2007 and 2012 datasets, while being conceptually more appealing as the very same model and loss are used at both training and test time.

Connected through hyperedge HE-1 (induced by source paper "daedalus breaking non maximum suppression in object detection via adversarial examples"):
  with members:
    - "semantic adversarial examples"
    - "model reuse attacks on deep learning systems"
    - "stochastic activation pruning for robust adversarial defense"
    - "adversarial patch"
    - "deep learning for generic object detection a survey"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "yolov3 an incremental improvement"
    - "multi task learning as multi objective optimization"

Connected through hyperedge HE-2 (induced by source paper "optimizing rank based metrics with blackbox differentiation"):
  with members:
    - "learning with average precision training image retrieval with a listwise loss"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "towards accurate one stage object detection with ap loss"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "sodeep a sorting deep net to learn ranking loss surrogates"
    - "mediaeval 2018 predicting media memorability task"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-3 (induced by source paper "learning with average precision training image retrieval with a listwise loss"):
  with members:
    - "local descriptors optimized for average precision"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "mihash online hashing with mutual information"
    - "in defense of the triplet loss for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "vse improving visual semantic embeddings with hard negatives"
    - "fine tuning cnn image retrieval with no human annotation"
    - "no fuss distance metric learning using proxies"

Connected through hyperedge HE-4 (induced by source paper "towards accurate one stage object detection with ap loss"):
  with members:
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"
    - "deformable convolutional networks"
    - "receptive field block net for accurate and fast object detection"
    - "single shot refinement neural network for object detection"
    - "dsod learning deeply supervised object detectors from scratch"
    - "an analysis of scale invariance in object detection snip"

Connected through hyperedge HE-5 (induced by source paper "comparison of object detection methods for crop damage assessment using deep learning"):
  with members:
    - "yolov3 an incremental improvement"
    - "deep learning in agriculture a survey"
    - "focal loss for dense object detection"
    - "using deep learning for image based plant disease detection"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"

Connected through hyperedge HE-6 (induced by source paper "learning to teach with dynamic loss functions"):
  with members:
    - "neural architecture optimization"
    - "interpretable and pedagogical examples"
    - "adversarial neural machine translation"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "learning to optimize"

Connected through hyperedge HE-7 (induced by source paper "resource constrained simultaneous detection and labeling of objects in high resolution satellite images"):
  with members:
    - "spacenet a remote sensing dataset and challenge series"
    - "yolov3 an incremental improvement"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "spatial memory for context reasoning in object detection"):
  with members:
    - "an implementation of faster rcnn with study for region sampling"
    - "neural map structured memory for deep reinforcement learning"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "cognitive mapping and planning for visual navigation"
    - "end to end instance segmentation and counting with recurrent attention"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

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)