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 "a review of object detection models based on convolutional neural network"):
  Abstract: Convolutional Neural Network (CNN) has become the state-of-the-art for object detection task. In this paper, we have explained different object detection models based on CNN. We have categorized those detection models according to two different approaches: two-stage approach and one-stage approach. Through this paper, we have shown advancements in object detection model from R-CNN to latest RefineDet. We have discussed the model description and training details of each model. We have also drawn a comparison among those models.

Includes members:
  Member "advancements in image classification using convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self supervised visual feature learning with curriculum")
  Member "ternausnetv2 fully convolutional network for instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "abdominal multi organ segmentation with cascaded convolutional and adversarial deep networks")
  Member "understanding batch normalization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on feature normalization and data augmentation")
  Member "focal loss for dense object detection":
    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 "single shot refinement neural network for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "revisiting the sibling head in object detector")
  Member "masklab instance segmentation by refining object detection with semantic and direction features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised instance segmentation by deep community learning")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "hard negative mining for metric learning based zero shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rethinking curriculum learning with incremental labels and adaptive compensation")

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)