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: "rgb t object tracking benchmark and baseline"
  Abstract: RGB-Thermal (RGB-T) object tracking receives more and more attention due to the strongly complementary benefits of thermal information to visible data. However, RGB-T research is limited by lacking a comprehensive evaluation platform. In this paper, we propose a large-scale video benchmark dataset for RGB-T tracking.It has three major advantages over existing ones: 1) Its size is sufficiently large for large-scale performance evaluation (total frame number: 234K, maximum frame per sequence: 8K). 2) The alignment between RGB-T sequence pairs is highly accurate, which does not need pre- or post-processing. 3) The occlusion levels are annotated for occlusion-sensitive performance analysis of different tracking algorithms.Moreover, we propose a novel graph-based approach to learn a robust object representation for RGB-T tracking. In particular, the tracked object is represented with a graph with image patches as nodes. This graph including graph structure, node weights and edge weights is dynamically learned in a unified ADMM (alternating direction method of multipliers)-based optimization framework, in which the modality weights are also incorporated for adaptive fusion of multiple source data.Extensive experiments on the large-scale dataset are executed to demonstrate the effectiveness of the proposed tracker against other state-of-the-art tracking methods. We also provide new insights and potential research directions to the field of RGB-T object tracking.

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"
    - "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"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"

Connected through hyperedge HE-2 (induced by source paper "m 5 l multi modal multi margin metric learning for rgbt tracking"):
  with members:
    - "multi modal fusion for end to end rgb t tracking"
    - "ranked list loss for deep metric learning"
    - "multi adapter rgbt tracking"
    - "dense feature aggregation and pruning for rgbt tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "real time mdnet"
    - "fanet quality aware feature aggregation network for rgb t tracking"
    - "end to end representation learning for correlation filter based tracking"

Connected through hyperedge HE-3 (induced by source paper "mdlatlrr a novel decomposition method for infrared and visible image fusion"):
  with members:
    - "an accelerated correlation filter tracker"
    - "joint group feature selection and discriminative filter learning for robust visual object tracking"
    - "infrared and visible image fusion with resnet and zero phase component analysis"
    - "densefuse a fusion approach to infrared and visible images"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "super resolution for hyperspectral and multispectral image fusion accounting for seasonal spectral variability"
    - "infrared and visible image fusion using a deep learning framework"
    - "image fusion with cosparse analysis operator"

Connected through hyperedge HE-4 (induced by source paper "vifb a visible and infrared image fusion benchmark"):
  with members:
    - "infrared and visible image fusion with resnet and zero phase component analysis"
    - "densefuse a fusion approach to infrared and visible images"
    - "infrared and visible image fusion using a deep learning framework"
    - "infrared and visible image fusion using latent low rank representation"
    - "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs"
    - "a novel performance evaluation methodology for single target trackers"
    - "medical image fusion a survey of the state of the art"

Connected through hyperedge HE-5 (induced by source paper "pedestrian attribute recognition a survey"):
  with members:
    - "the deeper the better analysis of person attributes recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "improving person re identification by attribute and identity learning"
    - "amc automl for model compression and acceleration on mobile devices"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "describe and attend to track learning natural language guided structural representation and visual attention for object tracking"
    - "large scale gan training for high fidelity natural image synthesis"
    - "localization guided learning for pedestrian attribute recognition"

Connected through hyperedge HE-6 (induced by source paper "segmenting objects in day and night edge conditioned cnn for thermal image semantic segmentation"):
  with members:
    - "synthetic data generation for end to end thermal infrared tracking"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "modulating early visual processing by language"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "learning visual reasoning without strong priors"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-7 (induced by source paper "multi modal fusion for end to end rgb t tracking"):
  with members:
    - "learning discriminative model prediction for tracking"
    - "synthetic data generation for end to end thermal infrared tracking"
    - "atom accurate tracking by overlap maximization"
    - "long term tracking in the wild a benchmark"
    - "distractor aware siamese networks for visual object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-8 (induced by source paper "dense feature aggregation and pruning for rgbt tracking"):
  with members:
    - "deeper and wider siamese networks for real time visual tracking"
    - "target aware deep tracking"
    - "real time mdnet"
    - "deep attentive tracking via reciprocative learning"
    - "domain generalization with domain specific aggregation modules"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "end to end representation learning for correlation filter based tracking"
    - "deep layer aggregation"

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)