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: "semantic foggy scene understanding with synthetic data"
  Abstract: This work addresses the problem of semantic foggy scene understanding (SFSU). Although extensive research has been performed on image dehazing and on semantic scene understanding with clear-weather images, little attention has been paid to SFSU. Due to the difficulty of collecting and annotating foggy images, we choose to generate synthetic fog on real images that depict clear-weather outdoor scenes, and then leverage these partially synthetic data for SFSU by employing state-of-the-art convolutional neural networks (CNN). In particular, a complete pipeline to add synthetic fog to real, clear-weather images using incomplete depth information is developed. We apply our fog synthesis on the Cityscapes dataset and generate Foggy Cityscapes with 20,550 images. SFSU is tackled in two ways: (1) with typical supervised learning, and (2) with a novel type of semi-supervised learning, which combines (1) with an unsupervised supervision transfer from clear-weather images to their synthetic foggy counterparts. In addition, we carefully study the usefulness of image dehazing for SFSU. For evaluation, we present Foggy Driving, a dataset with 101 real-world images depicting foggy driving scenes, which come with ground truth annotations for semantic segmentation and object detection. Extensive experiments show that (1) supervised learning with our synthetic data significantly improves the performance of state-of-the-art CNN for SFSU on Foggy Driving; (2) our semi-supervised learning strategy further improves performance; and (3) image dehazing marginally advances SFSU with our learning strategy. The datasets, models and code are made publicly available.

Connected through hyperedge HE-1 (induced by source paper "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "pixor real time 3d object detection from point clouds"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "robust video content alignment and compensation for clear vision through the rain"
    - "residual dense network for image super resolution"
    - "o haze a dehazing benchmark with real hazy and haze free outdoor images"
    - "image to image translation for domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "adapting object detectors with conditional domain normalization"):
  with members:
    - "compound domain adaptation in an open world"
    - "precise synthetic image and lidar presil dataset for autonomous vehicle perception"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "domain adaptation for structured output via discriminative patch representations"
    - "semantic image synthesis with spatially adaptive normalization"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "pointdan a multi scale 3d domain adaption network for point cloud representation"
    - "moment matching for multi source domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "unbiased mean teacher for cross domain object detection"):
  with members:
    - "self training and adversarial background regularization for unsupervised domain adaptive one stage object detection"
    - "scl towards accurate domain adaptive object detection via gradient detach based stacked complementary losses"
    - "multi adversarial faster rcnn for unrestricted object detection"
    - "exploring object relation in mean teacher for cross domain detection"
    - "a robust learning approach to domain adaptive object detection"
    - "automatic adaptation of object detectors to new domains using self training"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "wasserstein distance based domain adaptation for object detection"

Connected through hyperedge HE-4 (induced by source paper "unsupervised domain adaptive object detection using forward backward cyclic adaptation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "beyond sharing weights for deep domain adaptation"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "maximum entropy fine grained classification"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "co regularized alignment for unsupervised domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions"):
  with members:
    - "spatial attentive single image deraining with a high quality real rain dataset"
    - "progressive image deraining networks a better and simpler baseline"
    - "i can see clearly now image restoration via de raining"
    - "maskgan towards diverse and interactive facial image manipulation"
    - "robust semantic segmentation in adverse weather conditions by means of sensor data fusion"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "single image reflection removal exploiting misaligned training data and network enhancements"
    - "bisenet bilateral segmentation network for real time semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "ifan image instance full alignment networks for adaptive object detection"):
  with members:
    - "few shot adaptive faster r cnn"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "learning continuous face age progression a pyramid of gans"
    - "exploring object relation in mean teacher for cross domain detection"
    - "automatic adaptation of object detectors to new domains using self training"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "pixel and feature level based domain adaption for object detection in autonomous driving"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-7 (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"
    - "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"
    - "why do deep convolutional networks generalize so poorly to small image transformations"

Connected through hyperedge HE-8 (induced by source paper "deep domain adaptive object detection a survey"):
  with members:
    - "ifan image instance full alignment networks for adaptive object detection"
    - "few shot adaptive faster r cnn"
    - "self training and adversarial background regularization for unsupervised domain adaptive one stage object detection"
    - "scl towards accurate domain adaptive object detection via gradient detach based stacked complementary losses"
    - "borrow from anywhere pseudo multi modal object detection in thermal imagery"
    - "multi adversarial faster rcnn for unrestricted object detection"
    - "exploring object relation in mean teacher for cross domain detection"
    - "a robust learning approach to domain adaptive object detection"

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)