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: "a robust real time automatic license plate recognition based on the yolo detector"
  Abstract: Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications. However, many of the current solutions are still not robust in real-world situations, commonly depending on many constraints. This paper presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector. The Convolutional Neural Networks (CNNs) are trained and finetuned for each ALPR stage so that they are robust under different conditions (e.g., variations in camera, lighting, and background). Specially for character segmentation and recognition, we design a two-stage approach employing simple data augmentation tricks such as inverted License Plates (LPs) and flipped characters. The resulting ALPR approach achieved impressive results in two datasets. First, in the SSIG dataset, composed of 2,000 frames from 101 vehicle videos, our system achieved a recognition rate of 93.53% and 47 Frames Per Second (FPS), performing better than both Sighthound and OpenALPR commercial systems (89.80% and 93.03%, respectively) and considerably outperforming previous results (81.80%). Second, targeting a more realistic scenario, we introduce a larger public dataset1 dataset, designed to ALPR. This dataset contains 150 videos and 4,500 frames captured when both camera and vehicles are moving and also contains different types of vehicles (cars, motorcycles, buses and trucks). In our proposed dataset, the trial versions of commercial systems achieved recognition rates below 70%. On the other hand, our system performed better, with recognition rate of 78.33% and 35 FPS.The UFPR-ALPR dataset is publicly available to the research community at https://web.inf.ufpr.br/vri/databases/ufpr-alpr/ subject to privacy restrictions.

Connected through hyperedge HE-1 (induced by source paper "deep representations for cross spectral ocular biometrics"):
  with members:
    - "an efficient and layout independent automatic license plate recognition system based on the yolo detector"
    - "simultaneous iris and periocular region detection using coarse annotations"
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "the impact of preprocessing on deep representations for iris recognition on unconstrained environments"
    - "periocular recognition using cnn features off the shelf"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-2 (induced by source paper "cnn hyperparameter tuning applied to iris liveness detection"):
  with members:
    - "biometric presentation attack detection beyond the visible spectrum"
    - "an efficient and layout independent automatic license plate recognition system based on the yolo detector"
    - "simultaneous iris and periocular region detection using coarse annotations"
    - "convolutional neural networks for automatic meter reading"
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "presentation attack detection for iris recognition an assessment of the state of the art"
    - "deep representations for iris face and fingerprint spoofing detection"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "practical license plate recognition in unconstrained surveillance systems with adversarial super resolution"):
  with members:
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "lprnet license plate recognition via deep neural networks"
    - "yolov3 an incremental improvement"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "demystifying neural style transfer"
    - "segmentation free vehicle license plate recognition using convnet rnn"
    - "self adversarial training for human pose estimation"

Connected through hyperedge HE-4 (induced by source paper "snider single noisy image denoising and rectification for improving license plate recognition"):
  with members:
    - "driver drowsiness detection using condition adaptive representation learning framework"
    - "objects as points"
    - "cornernet lite efficient keypoint based object detection"
    - "yolov3 an incremental improvement"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "focusing attention towards accurate text recognition in natural images"
    - "reading car license plates using deep convolutional neural networks and lstms"

Connected through hyperedge HE-5 (induced by source paper "an efficient and layout independent automatic license plate recognition system based on the yolo detector"):
  with members:
    - "convolutional neural networks for automatic meter reading"
    - "yolov3 an incremental improvement"
    - "license plate detection and recognition using deeply learned convolutional neural networks"
    - "focal loss for dense object detection"
    - "cnn for license plate motion deblurring"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "spatially supervised recurrent convolutional neural networks for visual object tracking"

Connected through hyperedge HE-6 (induced by source paper "simultaneous iris and periocular region detection using coarse annotations"):
  with members:
    - "joint iris segmentation and localization using deep multi task learning framework"
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "the impact of preprocessing on deep representations for iris recognition on unconstrained environments"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "yolo9000 better faster stronger"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-7 (induced by source paper "vehicle re identification exploring feature fusion using multi stream convolutional networks"):
  with members:
    - "an efficient and layout independent automatic license plate recognition system based on the yolo detector"
    - "license plate detection and recognition using deeply learned convolutional neural networks"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "learning contextual dependence with convolutional hierarchical recurrent neural networks"
    - "you only look once unified real time object detection"
    - "evaluating two stream cnn for video classification"
    - "deep residual learning for image recognition"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "convolutional neural networks for automatic meter reading"):
  with members:
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "lcdet low complexity fully convolutional neural networks for object detection in embedded systems"
    - "yolo9000 better faster stronger"
    - "benchmark for license plate character segmentation"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "you only look once unified real time object detection"
    - "deep learning in neural networks"

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)