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: "synthetic data generation for end to end thermal infrared tracking"
  Abstract: The usage of both off-the-shelf and end-to-end trained deep networks have significantly improved the performance of visual tracking on RGB videos. However, the lack of large labeled datasets hampers the usage of convolutional neural networks for tracking in thermal infrared (TIR) images. Therefore, most state-of-the-art methods on tracking for TIR data are still based on handcrafted features. To address this problem, we propose to use image-to-image translation models. These models allow us to translate the abundantly available labeled RGB data to synthetic TIR data. We explore both the usage of paired and unpaired image translation models for this purpose. These methods provide us with a large labeled dataset of synthetic TIR sequences, on which we can train end-to-end optimal features for tracking. To the best of our knowledge, we are the first to train end-to-end features for TIR tracking. We perform extensive experiments on the VOT-TIR2017 dataset. We show that a network trained on a large dataset of synthetic TIR data obtains better performance than one trained on the available real TIR data. Combining both data sources leads to further improvement. In addition, when we combine the network with motion features, we outperform the state of the art with a relative gain of over 10%, clearly showing the efficiency of using synthetic data to train end-to-end TIR trackers.

Connected through hyperedge HE-1 (induced by source paper "segmenting objects in day and night edge conditioned cnn for thermal image semantic segmentation"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "rgb t object tracking benchmark and baseline"
    - "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-2 (induced by source paper "multi modal fusion for end to end rgb t tracking"):
  with members:
    - "learning discriminative model prediction for tracking"
    - "atom accurate tracking by overlap maximization"
    - "long term tracking in the wild a benchmark"
    - "distractor aware siamese networks for visual object tracking"
    - "rgb t object tracking benchmark and baseline"
    - "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-3 (induced by source paper "controlling biases and diversity in diverse image to image translation"):
  with members:
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "identifying and correcting label bias in machine learning"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "women also snowboard overcoming bias in captioning models"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "multimodal unsupervised image to image translation"
    - "weakly supervised domain specific color naming based on attention"

Connected through hyperedge HE-4 (induced by source paper "multi task driven feature models for thermal infrared tracking"):
  with members:
    - "ptb tir a thermal infrared pedestrian tracking benchmark"
    - "learning deep multi level similarity for thermal infrared object tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "target aware deep tracking"
    - "large margin structured convolution operator for thermal infrared object tracking"
    - "a twofold siamese network for real time object tracking"
    - "vital visual tracking via adversarial learning"
    - "learning spatial temporal regularized correlation filters for visual tracking"

Connected through hyperedge HE-5 (induced by source paper "domain adaptation for vehicle detection from bird s eye view lidar point cloud data"):
  with members:
    - "mapless online detection of dynamic objects in 3d lidar"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "yolov3 an incremental improvement"
    - "deep visual domain adaptation a survey"
    - "deep semantic classification for 3d lidar data"
    - "carla an open urban driving simulator"
    - "adversarial discriminative domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "rgb t object tracking benchmark and baseline"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "visual tracking via dynamic graph learning"
    - "dynamic label graph matching for unsupervised video re identification"
    - "eco efficient convolution operators for tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "learning spatially regularized correlation filters for visual tracking"
    - "learning multi domain convolutional neural networks for visual tracking"

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)