human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "empirical upper bound in object detection and more"):
  Abstract: Object detection remains as one of the most notorious open problems in computer vision. Despite large strides in accuracy in recent years, modern object detectors have started to saturate on popular benchmarks raising the question of how far we can reach with deep learning tools and tricks. Here, by employing 2 state-of-the-art object detection benchmarks, and analyzing more than 15 models over 4 large scale datasets, we I) carefully determine the upperbound in AP, which is 91.6% on VOC (test2007), 78.2% on COCO (val2017), and 58.9% on OpenImages V4 (validation), regardless of the IOU. These numbers are much better than the mAP of the best model1 (47.9% on VOC, and 46.9% on COCO; IOUs=.5:.95), II) characterize the sources of errors in object detectors, in a novel and intuitive way, and find that classification error (confusion with other classes and misses) explains the largest fraction of errors and weighs more than localization and duplicate errors, and III) analyze the invariance properties of models when surrounding context of an object is removed, when an object is placed in an incongruent background, and when images are blurred or flipped vertically. We find that models generate boxes on empty regions and that context is more important for detecting small objects than larger ones. Our work taps into the tight relationship between recognition and detection and offers insights for building better models.

Includes members:
  Member "an analysis of pre training on object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural data server a large scale search engine for transfer learning data")
  Member "libra r cnn towards balanced learning for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "forward and backward information retention for accurate binary neural networks")
  Member "generalized intersection over union a metric and a loss for bounding box regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "efficientdet scalable and efficient object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "an empirical study of spatial attention mechanisms in deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attentive group equivariant convolutional networks")
  Member "contextual relabelling of detected objects":
    also connected through other hyperedges:
      (none)
  Member "mmdetection open mmlab detection toolbox and benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mixpath a unified approach for one shot neural architecture search")
  Member "mask scoring r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "proposal learning for semi supervised object detection")

Question: Please tell me which class the source paper 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)