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: "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding"
  Abstract: This work addresses the problem of semantic scene understanding under fog. Although marked progress has been made in semantic scene understanding, it is mainly concentrated on clear-weather scenes. Extending semantic segmentation methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both labeled synthetic foggy data and unlabeled real foggy data. The method is based on the fact that the results of semantic segmentation in moderately adverse conditions (light fog) can be bootstrapped to solve the same problem in highly adverse conditions (dense fog). CMAda is extensible to other adverse conditions and provides a new paradigm for learning with synthetic data and unlabeled real data. In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) a novel fog densification method to densify the fog in real foggy scenes without using depth; and 4) the Foggy Zurich dataset comprising 3808 real foggy images, with pixel-level semantic annotations for 40 images under dense fog. Our experiments show that 1) our fog simulation and fog density estimator outperform their state-of-the-art counterparts with respect to the task of semantic foggy scene understanding (SFSU); 2) CMAda improves the performance of state-of-the-art models for SFSU significantly, benefiting both from our synthetic and real foggy data. The datasets and code are available at the project website.

Connected through hyperedge HE-1 (induced by source paper "night time semantic segmentation with a large real dataset"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "spgnet semantic prediction guidance for scene parsing"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "semantic foggy scene understanding with synthetic data"

Connected through hyperedge HE-2 (induced by source paper "see clearer at night towards robust nighttime semantic segmentation through day night image conversion"):
  with members:
    - "semantic segmentation of panoramic images using a synthetic dataset"
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "panoramic annular localizer tackling the variation challenges of outdoor localization using panoramic annular images and active deep descriptors"
    - "don t worry about the weather unsupervised condition dependent domain adaptation"
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "segmenting objects in day and night edge conditioned cnn for thermal image semantic segmentation"
    - "i can see clearly now image restoration via de raining"
    - "a cross season correspondence dataset for robust semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "discovering latent classes for semi supervised semantic segmentation"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "curriculum based dropout discriminator for domain adaptation"
    - "frame to frame aggregation of active regions in web videos for weakly supervised semantic segmentation"
    - "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "constructing self motivated pyramid curriculums for cross domain semantic segmentation a non adversarial approach"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"

Connected through hyperedge HE-4 (induced by source paper "domain bridge for unpaired image to image translation and unsupervised domain adaptation"):
  with members:
    - "i can see clearly now image restoration via de raining"
    - "towards instance level image to image translation"
    - "physics based rendering for improving robustness to rain"
    - "few shot unsupervised image to image translation"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "adversarial learning and self teaching techniques for domain adaptation in semantic segmentation"
    - "label propagation for deep semi supervised learning"
    - "learning across tasks and domains"

Connected through hyperedge HE-5 (induced by source paper "constructing self motivated pyramid curriculums for cross domain semantic segmentation a non adversarial approach"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "a curriculum domain adaptation approach to the semantic segmentation of urban scenes"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "advent adversarial entropy minimization for domain adaptation in semantic segmentation"
    - "image to image translation for domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"):
  with members:
    - "panoptic segmentation"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "semantic foggy scene understanding with synthetic data"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"

Connected through hyperedge HE-7 (induced by source paper "multi task learning for automotive foggy scene understanding via domain adaptation to an illumination invariant representation"):
  with members:
    - "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"
    - "incorporating luminance depth and color information by fusion based networks for semantic segmentation"
    - "skip connections eliminate singularities"
    - "demystifying neural style transfer"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "semantic understanding of foggy scenes with purely synthetic data"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "semantic nighttime image segmentation with synthetic stylized data gradual adaptation and uncertainty aware evaluation"
    - "seeing through fog without seeing fog deep sensor fusion in the absence of labeled training data"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "semantic foggy scene understanding with synthetic data"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

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)