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 "gated context aggregation network for image dehazing and deraining"):
  Abstract: Image dehazing aims to recover the uncorrupted content from a hazy image. Instead of leveraging traditional low-level or handcrafted image priors as the restoration constraints, e.g., dark channels and increased contrast, we propose an end-to-end gated context aggregation network to directly restore the final haze-free image. In this network, we adopt the latest smoothed dilation technique to help remove the gridding artifacts caused by the widely-used dilated convolution with negligible extra parameters, and leverage a gated sub-network to fuse the features from different levels. Extensive experiments demonstrate that our method can surpass previous state-of-the-art methods by a large margin both quantitatively and qualitatively. In addition, to demonstrate the generality of the proposed method, we further apply it to the image deraining task, which also achieves the state-of-the-art performance. Code has been made available at this https URL.

Includes members:
  Member "gated fusion network for single image dehazing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "recurrent squeeze and excitation context aggregation net for single image deraining":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")
  Member "density aware single image de raining using a multi stream dense network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")
  Member "deep exemplar based colorization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chromagan adversarial picture colorization with semantic class distribution")
  Member "smoothed dilated convolutions for improved dense prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "arma nets expanding receptive field for dense prediction")
  Member "decouple learning for parameterized image operators":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "meta sr a magnification arbitrary network for super resolution")
  Member "densely connected pyramid dehazing network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "clearing the skies a deep network architecture for single image rain removal":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments")

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)