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 "semantic segmentation of panoramic images using a synthetic dataset"):
  Abstract: Panoramic images have advantages in information capacity and scene stability due to their large field of view (FoV). In this paper, we propose a method to synthesize a new dataset of panoramic image. We managed to stitch the images taken from different directions into panoramic images, together with their labeled images, to yield the panoramic semantic segmentation dataset denominated as SYNTHIA-PANO. For the purpose of finding out the effect of using panoramic images as training dataset, we designed and performed a comprehensive set of experiments. Experimental results show that using panoramic images as training data is beneficial to the segmentation result. In addition, it has been shown that by using panoramic images with a 180 degree FoV as training data the model has better performance. Furthermore, the model trained with panoramic images also has a better capacity to resist the image distortion.

Includes members:
  Member "panoramic annular localizer tackling the variation challenges of outdoor localization using panoramic annular images and active deep descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "see clearer at night towards robust nighttime semantic segmentation through day night image conversion")
  Member "acnet attention based network to exploit complementary features for rgbd semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "real time fusion network for rgb d semantic segmentation incorporating unexpected obstacle detection for road driving images")
  Member "restricted deformable convolution based road scene semantic segmentation using surround view cameras":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "universal semantic segmentation for fisheye urban driving images")
  Member "unsupervised learning of depth and ego motion from panoramic video":
    also connected through other hyperedges:
      (none)
  Member "importance aware semantic segmentation with efficient pyramidal context network for navigational assistant systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "see clearer at night towards robust nighttime semantic segmentation through day night image conversion")
  Member "orientation aware semantic segmentation on icosahedron spheres":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ds pass detail sensitive panoramic annular semantic segmentation through swaftnet for surrounding sensing")
  Member "adapting semantic segmentation models for changes in illumination and camera perspective":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semantic sensor fusion from camera to sparse lidar information")
  Member "a comparative study of high recall real time semantic segmentation based on swift factorized network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "see clearer at night towards robust nighttime semantic segmentation through day night image conversion")

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)