human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "haze visibility enhancement a survey and quantitative benchmarking"
  Abstract: This paper provides a comprehensive survey of methods dealing with visibility enhancement of images taken in hazy or foggy scenes. The survey begins with discussing the optical models of atmospheric scattering media and image formation. This is followed by a survey of existing methods, which are grouped to multiple image methods, polarizing filters based methods, methods with known depth, and single-image methods. We also provide a benchmark of a number of well known single-image methods, based on a recent dataset provided by Fattal and our newly generated scattering media dataset that contains ground truth images for quantitative evaluation. To our knowledge, this is the first benchmark using numerical metrics to evaluate dehazing techniques. This benchmark allows us to objectively compare the results of existing methods and to better identify the strengths and limitations of each method.

Connected through hyperedge HE-1 (induced by source paper "a little fog for a large turn"):
  with members:
    - "natural adversarial examples"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "semantic foggy scene understanding with synthetic data"
    - "adversarial patch"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "exploring the limits of weakly supervised pretraining"
    - "idd a dataset for exploring problems of autonomous navigation in unconstrained environments"
    - "audio adversarial examples targeted attacks on speech to text"

Connected through hyperedge HE-2 (induced by source paper "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding"):
  with members:
    - "real time 3d traffic cone detection for autonomous driving"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "learning to adapt structured output space for semantic segmentation"
    - "semantic foggy scene understanding with synthetic data"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "learning converged propagations with deep prior ensemble for image enhancement"):
  with members:
    - "clearing the skies a deep network architecture for single image rain removal"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "dehazenet an end to end system for single image haze removal"
    - "deep joint rain detection and removal from a single image"
    - "cascades of regression tree fields for image restoration"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "model adaptation with synthetic and real data for semantic dense foggy scene understanding"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "learning driving models with a surround view camera system and a route planner"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning from synthetic data addressing domain shift for semantic segmentation"
    - "joint transmission map estimation and dehazing using deep networks"

Connected through hyperedge HE-5 (induced by source paper "semantic foggy scene understanding with synthetic data"):
  with members:
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "cycada cycle consistent adversarial domain adaptation"
    - "joint transmission map estimation and dehazing using deep networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "playing for data ground truth from computer games"

Connected through hyperedge HE-6 (induced by source paper "gated fusion network for single image dehazing"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "cross domain human parsing via adversarial feature and label adaptation"
    - "densely connected pyramid dehazing network"
    - "benchmarking single image dehazing and beyond"
    - "image de raining using a conditional generative adversarial network"
    - "learning to generate time lapse videos using multi stage dynamic generative adversarial networks"
    - "temporal dynamic graph lstm for action driven video object detection"
    - "deep image harmonization"

Connected through hyperedge HE-7 (induced by source paper "progressive feature fusion network for realistic image dehazing"):
  with members:
    - "gated fusion network for single image dehazing"
    - "o haze a dehazing benchmark with real hazy and haze free outdoor images"
    - "benchmarking single image dehazing and beyond"
    - "dehazenet an end to end system for single image haze removal"
    - "wide residual networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "cycle dehaze enhanced cyclegan for single image dehazing"):
  with members:
    - "candy conditional adversarial networks based fully end to end system for single image haze removal"
    - "o haze a dehazing benchmark with real hazy and haze free outdoor images"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "joint transmission map estimation and dehazing using deep networks"
    - "dehazenet an end to end system for single image haze removal"
    - "enhancenet single image super resolution through automated texture synthesis"
    - "very deep convolutional networks for large scale image recognition"

Question: Please tell me which class the center node 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)