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 understanding of foggy scenes with purely synthetic data"):
  Abstract: This work addresses the problem of semantic scene understanding under foggy road conditions. Although marked progress has been made in semantic scene understanding over the recent years, it is mainly concentrated on clear weather outdoor scenes. Extending semantic segmentation methods to adverse weather conditions like fog is crucially important for outdoor applications such as self-driving cars. In this paper, we propose a novel method, which uses purely synthetic data to improve the performance on unseen real-world foggy scenes captured in the streets of Zurich and its surroundings. Our results highlight the potential and power of photo-realistic synthetic images for training and especially fine-tuning deep neural nets. Our contributions are threefold, 1) we created a purely synthetic, high-quality foggy dataset of 25,000 unique outdoor scenes, that we call Foggy Synscapes and plan to release publicly 2) we show that with this data we outperform previous approaches on real-world foggy test data 3) we show that a combination of our data and previously used data can even further improve the performance on real-world foggy data.

Includes members:
  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 "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "night time semantic segmentation with a large real dataset")
  Member "semantic nighttime image segmentation with synthetic stylized data gradual adaptation and uncertainty aware evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "see clearer at night towards robust nighttime semantic segmentation through day night image conversion")
  Member "seeing through fog without seeing fog deep sensor fusion in the absence of labeled training data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "uncertainty depth estimation with gated images for 3d reconstruction")
  Member "model adaptation with synthetic and real data for semantic dense foggy scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency")
  Member "bisenet bilateral segmentation network for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "semantic foggy scene understanding with synthetic data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather")
  Member "dark model adaptation semantic image segmentation from daytime to nighttime":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency")

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)