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: "dark model adaptation semantic image segmentation from daytime to nighttime"
  Abstract: This work addresses the problem of semantic image segmentation of nighttime scenes. Although considerable progress has been made in semantic image segmentation, it is mainly related to daytime scenarios. This paper proposes a novel method to progressive adapt the semantic models trained on daytime scenes, along with large-scale annotations therein, to nighttime scenes via the bridge of twilight time -- the time between dawn and sunrise, or between sunset and dusk. The goal of the method is to alleviate the cost of human annotation for nighttime images by transferring knowledge from standard daytime conditions. In addition to the method, a new dataset of road scenes is compiled; it consists of 35,000 images ranging from daytime to twilight time and to nighttime. Also, a subset of the nighttime images are densely annotated for method evaluation. Our experiments show that our method is effective for model adaptation from daytime scenes to nighttime scenes, without using extra human annotation.

Connected through hyperedge HE-1 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency"):
  with members:
    - "recover and identify a generative dual model for cross resolution person re identification"
    - "learning correspondence from the cycle consistency of time"
    - "learning resolution invariant deep representations for person re identification"
    - "show match and segment joint learning of semantic matching and object co segmentation"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "learning to adapt structured output space for semantic segmentation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "deepmvs learning multi view stereopsis"

Connected through hyperedge HE-2 (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"
    - "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "heatnet bridging the day night domain gap in semantic segmentation with thermal images"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "pst900 rgb thermal calibration dataset and segmentation network"
    - "segmenting objects in day and night edge conditioned cnn for thermal image semantic segmentation"
    - "see clearer at night towards robust nighttime semantic segmentation through day night image conversion"
    - "an adversarial perturbation oriented domain adaptation approach for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "rgb t object tracking benchmark and baseline"

Connected through hyperedge HE-4 (induced by source paper "benchmarking the robustness of semantic segmentation models"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "adversarial examples are a natural consequence of test error in noise"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "deepcorrect correcting dnn models against image distortions"
    - "semantic foggy scene understanding with synthetic data"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-5 (induced by source paper "revisiting self supervised visual representation learning"):
  with members:
    - "grasp2vec learning object representations from self supervised grasping"
    - "learning image representations by completing damaged jigsaw puzzles"
    - "boosting self supervised learning via knowledge transfer"
    - "self supervised learning of a facial attribute embedding from video"
    - "deep clustering for unsupervised learning of visual features"
    - "cross and learn cross modal self supervision"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"

Connected through hyperedge HE-6 (induced by source paper "benchmarking robustness in object detection autonomous driving when winter is coming"):
  with members:
    - "d 2 city a large scale dashcam video dataset of diverse traffic scenarios"
    - "rain removal in traffic surveillance does it matter"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "fixing the train test resolution discrepancy"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "semantic foggy scene understanding with synthetic data"

Connected through hyperedge HE-7 (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-8 (induced by source paper "online adaptation through meta learning for stereo depth estimation"):
  with members:
    - "real time self adaptive deep stereo"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "boosting domain adaptation by discovering latent domains"
    - "structured attention guided convolutional neural fields for monocular depth estimation"

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)