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 "data augmentation for object detection via progressive and selective instance switching"):
  Abstract: Collection of massive well-annotated samples is effective in improving object detection performance but is extremely laborious and costly. Instead of data collection and annotation, the recently proposed Cut-Paste methods [12, 15] show the potential to augment training dataset by cutting foreground objects and pasting them on proper new backgrounds. However, existing Cut-Paste methods cannot guarantee synthetic images always precisely model visual context, and all of them require external datasets. To handle above issues, this paper proposes a simple yet effective instance-switching (IS) strategy, which generates new training data by switching instances of same class from different images. Our IS naturally preserves contextual coherence in the original images while requiring no external dataset. For guiding our IS to obtain better object performance, we explore issues of instance imbalance and class importance in datasets, which frequently occur and bring adverse effect on detection performance. To this end, we propose a novel Progressive and Selective Instance-Switching (PSIS) method to augment training data for object detection. The proposed PSIS enhances instance balance by combining selective re-sampling with a class-balanced loss, and considers class importance by progressively augmenting training dataset guided by detection performance. The experiments are conducted on the challenging MS COCO benchmark, and results demonstrate our PSIS brings clear improvement over various state-of-the-art detectors (e.g., Faster R-CNN, FPN, Mask R-CNN and SNIPER), showing the superiority and generality of our PSIS. Code and models are available at: this https URL.

Includes members:
  Member "data augmentation using random image cropping and patching for deep cnns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding and enhancing mixed sample data augmentation")
  Member "dynamic curriculum learning for imbalanced data classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "forward and backward information retention for accurate binary neural networks")
  Member "class balanced loss based on effective number of samples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")
  Member "learning to segment via cut and paste":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "data augmentation by pairing samples for images classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "on the importance of visual context for data augmentation in scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "instance segmentation with point supervision")
  Member "compositional gan learning conditional image composition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptive composition gan towards realistic image synthesis")
  Member "modeling visual context is key to augmenting object detection datasets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mixup regularization for region proposal based object detectors")

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)