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 "panda panoptic data augmentation"):
  Abstract: The recently proposed panoptic segmentation task presents a significant challenge of image understanding with computer vision by unifying semantic segmentation and instance segmentation tasks. In this paper we present an efficient and novel panoptic data augmentation (PanDA) method which operates exclusively in pixel space, requires no additional data or training, and is computationally cheap to implement. We retrain the original state-of-the-art UPSNet panoptic segmentation model on PanDA augmented Cityscapes dataset, and demonstrate all-round performance improvement upon the original model. We also show that PanDA is effective across scales from 10 to 30,000 images, as well as generalizable to Microsoft COCO panoptic segmentation task. Finally, the effectiveness of PanDA generated unrealistic-looking training images suggest that we should rethink about optimizing levels of image realism for efficient data augmentation.

Includes members:
  Member "panoptic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "upsnet a unified panoptic segmentation network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep affinity net instance segmentation via affinity")
  Member "seamless scene segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semantically guided representation learning for self supervised monocular depth")
  Member "ssap single shot instance segmentation with affinity pyramid":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards bounding box free panoptic segmentation")
  Member "synthetic examples improve generalization for rare classes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tackling climate change with machine learning")
  Member "gan augmentation augmenting training data using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on leveraging pretrained gans for limited data generation")
  Member "autoaugment learning augmentation policies from data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "opengan open set generative adversarial 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)