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 "cooperative semantic segmentation and image restoration in adverse environmental conditions"):
  Abstract: Most state-of-the-art semantic segmentation approaches only achieve high accuracy in good conditions. In practically-common but less-discussed adverse environmental conditions, their performance can decrease enormously. Existing studies usually cast the handling of segmentation in adverse conditions as a separate post-processing step after signal restoration, making the segmentation performance largely depend on the quality of restoration. In this paper, we propose a novel deep-learning framework to tackle semantic segmentation and image restoration in adverse environmental conditions in a holistic manner. The proposed approach contains two components: Semantically-Guided Adaptation, which exploits semantic information from degraded images to refine the segmentation; and Exemplar-Guided Synthesis, which restores images from semantic label maps given degraded exemplars as the guidance. Our method cooperatively leverages the complementarity and interdependence of low-level restoration and high-level segmentation in adverse environmental conditions. Extensive experiments on various datasets demonstrate that our approach can not only improve the accuracy of semantic segmentation with degradation cues, but also boost the perceptual quality and structural similarity of image restoration with semantic guidance.

Includes members:
  Member "spatial attentive single image deraining with a high quality real rain dataset":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "let s get dirty gan based data augmentation for soiling and adverse weather classification in autonomous driving")
  Member "progressive image deraining networks a better and simpler baseline":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "let s get dirty gan based data augmentation for soiling and adverse weather classification in autonomous driving")
  Member "i can see clearly now image restoration via de raining":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rainy screens collecting rainy datasets indoors")
  Member "maskgan towards diverse and interactive facial image manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")
  Member "robust semantic segmentation in adverse weather conditions by means of sensor data fusion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "see clearer at night towards robust nighttime semantic segmentation through day night image conversion")
  Member "multi channel attention selection gan with cascaded semantic guidance for cross view image translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "single image reflection removal exploiting misaligned training data and network enhancements":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reflection prior")
  Member "bisenet bilateral segmentation network for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "highly efficient salient object detection with 100k parameters")

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)