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 "see clearer at night towards robust nighttime semantic segmentation through day night image conversion"):
  Abstract: Currently, semantic segmentation shows remarkable efficiency and reliability in standard scenarios such as daytime scenes with favorable illumination conditions. However, in face of adverse conditions such as the nighttime, semantic segmentation loses its accuracy significantly. One of the main causes of the problem is the lack of sufficient annotated segmentation datasets of nighttime scenes. In this paper, we propose a framework to alleviate the accuracy decline when semantic segmentation is taken to adverse conditions by using Generative Adversarial Networks (GANs). To bridge the daytime and nighttime image domains, we made key observation that compared to datasets in adverse conditions, there are considerable amount of segmentation datasets in standard conditions such as BDD and our collected ZJU datasets. Our GAN-based nighttime semantic segmentation framework includes two methods. In the first method, GANs were used to translate nighttime images to the daytime, thus semantic segmentation can be performed using robust models already trained on daytime datasets. In another method, we use GANs to translate different ratio of daytime images in the dataset to the nighttime but still with their labels. In this sense, synthetic nighttime segmentation datasets can be generated to yield models prepared to operate at nighttime conditions robustly. In our experiment, the later method significantly boosts the performance at the nighttime evidenced by quantitative results using Intersection over Union (IoU) and Pixel Accuracy (Acc). We show that the performance varies with respect to the proportion of synthetic nighttime images in the dataset, where the sweet spot corresponds to most robust performance across the day and night.

Includes members:
  Member "semantic segmentation of panoramic images using a synthetic dataset":
    also connected through other hyperedges:
      (none)
  Member "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather")
  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-2 (induced by source paper "a comparative study of high recall real time semantic segmentation based on swift factorized network")
  Member "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "night time semantic segmentation with a large real dataset")
  Member "don t worry about the weather unsupervised condition dependent domain adaptation":
    also connected through other hyperedges:
      (none)
  Member "acnet attention based network to exploit complementary features for rgbd semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "real time fusion network for rgb d semantic segmentation incorporating unexpected obstacle detection for road driving images")
  Member "segmenting objects in day and night edge conditioned cnn for thermal image semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "heatnet bridging the day night domain gap in semantic segmentation with thermal images")
  Member "i can see clearly now image restoration via de raining":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")

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)