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: "segmenting objects in day and night edge conditioned cnn for thermal image semantic segmentation"
  Abstract: Despite much research progress in image semantic segmentation, it remains challenging under adverse environmental conditions caused by imaging limitations of visible spectrum. While thermal infrared cameras have several advantages over cameras for the visible spectrum, such as operating in total darkness, insensitive to illumination variations, robust to shadow effects and strong ability to penetrate haze and smog. These advantages of thermal infrared cameras make the segmentation of semantic objects in day and night. In this paper, we propose a novel network architecture, called edge-conditioned convolutional neural network (EC-CNN), for thermal image semantic segmentation. Particularly, we elaborately design a gated feature-wise transform layer in EC-CNN to adaptively incorporate edge prior knowledge. The whole EC-CNN is end-to-end trained, and can generate high-quality segmentation results with the edge guidance. Meanwhile, we also introduce a new benchmark dataset named "Segment Objects in Day And night"(SODA) for comprehensive evaluations in thermal image semantic segmentation. SODA contains over 7,168 manually annotated and synthetically generated thermal images with 20 semantic region labels and from a broad range of viewpoints and scene complexities. Extensive experiments on SODA demonstrate the effectiveness of the proposed EC-CNN against the state-of-the-art methods.

Connected through hyperedge HE-1 (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"
    - "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"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"

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"
    - "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"
    - "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 "fanet quality aware feature aggregation network for robust rgb t tracking"):
  with members:
    - "multi adapter rgbt tracking"
    - "learning target oriented dual attention for robust rgb t tracking"
    - "selective kernel networks"
    - "dense feature aggregation and pruning for rgbt tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "real time mdnet"
    - "rgb t object tracking benchmark and baseline"
    - "meta tracker fast and robust online adaptation for visual object trackers"

Connected through hyperedge HE-4 (induced by source paper "fanet quality aware feature aggregation network for rgb t tracking"):
  with members:
    - "multi adapter rgbt tracking"
    - "learning target oriented dual attention for robust rgb t tracking"
    - "selective kernel networks"
    - "dense feature aggregation and pruning for rgbt tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "real time mdnet"
    - "rgb t object tracking benchmark and baseline"
    - "meta tracker fast and robust online adaptation for visual object trackers"

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)