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 "dr loss improving object detection by distributional ranking"):
  Abstract: Most of object detection algorithms can be categorized into two classes: two-stage detectors and one-stage detectors. For two-stage detectors, a region proposal phase can filter massive background candidates in the first stage and it masks the classification task more balanced in the second stage. Recently, one-stage detectors have attracted much attention due to its simple yet effective architecture. Different from two-stage detectors, one-stage detectors have to identify foreground objects from all candidates in a single stage. This architecture is efficient but can suffer from the imbalance issue with respect to two aspects: the imbalance between classes and that in the distribution of background, where only a few candidates are hard to be identified. In this work, we propose to address the challenge by developing the distributional ranking (DR) loss. First, we convert the classification problem to a ranking problem to alleviate the class-imbalance problem. Then, we propose to rank the distribution of foreground candidates above that of background ones in the constrained worst-case scenario. This strategy not only handles the imbalance in background candidates but also improves the efficiency for the ranking algorithm. Besides the classification task, we also improve the regression loss by gradually approaching the $L_1$ loss as suggested in interior-point methods. To evaluate the proposed losses, we replace the corresponding losses in RetinaNet that reports the state-of-the-art performance as a one-stage detector. With the ResNet-101 as the backbone, our method can improve mAP on COCO data set from $39.1\%$ to $41.1\%$ by only changing the loss functions and it verifies the effectiveness of the proposed losses.

Includes members:
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "cascade r cnn delving into high quality object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "proposal learning for semi supervised object detection")
  Member "dssd deconvolutional single shot detector":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "deformable convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "accurate large minibatch sgd training imagenet in 1 hour":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning representations by predicting bags of visual words")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "aggregated residual transformations for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "ssd single shot multibox detector":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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)