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 nighttime image segmentation with synthetic stylized data gradual adaptation and uncertainty aware evaluation"
  Abstract: This work addresses the problem of semantic segmentation of nighttime images. The main direction of recent progress in semantic segmentation pertains to daytime scenes with favorable illumination conditions. We focus on improving the performance of state-of-the-art methods on the nighttime domain by adapting them to nighttime data without extra annotations, and designing a new evaluation framework to address the uncertainty of semantics in nighttime images. To this end, we make the following contributions: 1) a novel pipeline for dataset-scale guided style transfer to generate synthetic nighttime images from real daytime input; 2) a framework to gradually adapt semantic segmentation models from day to night via stylized and real images of progressively increasing darkness; 3) a novel uncertainty-aware annotation and evaluation framework and metric for semantic segmentation in adverse conditions; 4) the Dark Zurich dataset with 2416 nighttime and 2920 twilight unlabeled images plus 20 nighttime images with pixel-level annotations that conform to our newly-proposed evaluation. Our experiments evidence that both our stylized data per se and our gradual adaptation significantly boost performance at nighttime both for standard evaluation metrics and our metric. Moreover, our new evaluation reveals that state-of-the-art segmentation models output overly confident predictions at indiscernible regions compared to visible ones.

Connected through hyperedge HE-1 (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"
    - "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "don t worry about the weather unsupervised condition dependent domain adaptation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "map management for efficient long term visual localization in outdoor environments"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "night to day image translation for retrieval based localization"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "texture underfitting for domain adaptation"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "domain adaptation for structured output via discriminative patch representations"
    - "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"
    - "deep visual domain adaptation a survey"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"

Connected through hyperedge HE-4 (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"
    - "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding"
    - "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)