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: "reading car license plates using deep convolutional neural networks and lstms"
  Abstract: In this work, we tackle the problem of car license plate detection and recognition in natural scene images. Inspired by the success of deep neural networks (DNNs) in various vision applications, here we leverage DNNs to learn high-level features in a cascade framework, which lead to improved performance on both detection and recognition. #R##N#Firstly, we train a $37$-class convolutional neural network (CNN) to detect all characters in an image, which results in a high recall, compared with conventional approaches such as training a binary text/non-text classifier. False positives are then eliminated by the second plate/non-plate CNN classifier. Bounding box refinement is then carried out based on the edge information of the license plates, in order to improve the intersection-over-union (IoU) ratio. The proposed cascade framework extracts license plates effectively with both high recall and precision. Last, we propose to recognize the license characters as a {sequence labelling} problem. A recurrent neural network (RNN) with long short-term memory (LSTM) is trained to recognize the sequential features extracted from the whole license plate via CNNs. The main advantage of this approach is that it is segmentation free. By exploring context information and avoiding errors caused by segmentation, the RNN method performs better than a baseline method of combining segmentation and deep CNN classification; and achieves state-of-the-art recognition accuracy.

Connected through hyperedge HE-1 (induced by source paper "a pooling based scene text proposal technique for scene text reading in the wild"):
  with members:
    - "an end to end textspotter with explicit alignment and attention"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "wordsup exploiting word annotations for character based text detection"
    - "wordfence text detection in natural images with border awareness"
    - "icdar2017 competition on reading chinese text in the wild rctw 17"
    - "wetext scene text detection under weak supervision"
    - "deep direct regression for multi oriented scene text detection"
    - "r2cnn rotational region cnn for orientation robust scene text detection"

Connected through hyperedge HE-2 (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"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "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"

Connected through hyperedge HE-3 (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"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "spot evasion attacks adversarial examples for license plate recognition systems with convolution neural networks"):
  with members:
    - "adversarial patch"
    - "robust physical world attacks on deep learning models"
    - "license plate detection and recognition using deeply learned convolutional neural networks"
    - "boosting adversarial attacks with momentum"
    - "synthesizing robust adversarial examples"
    - "yolo9000 better faster stronger"
    - "why should i trust you explaining the predictions of any classifier"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-5 (induced by source paper "delp dar system for license plate detection and recognition"):
  with members:
    - "swish a self gated activation function"
    - "mask r cnn"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-6 (induced by source paper "deep learning based segmentation free license plate recognition using roadway surveillance camera images"):
  with members:
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-7 (induced by source paper "accurate data efficient unconstrained text recognition with convolutional neural networks"):
  with members:
    - "an efficient end to end neural model for handwritten text recognition"
    - "scene text recognition from two dimensional perspective"
    - "handwriting recognition of historical documents with few labeled data"
    - "autoaugment learning augmentation policies from data"
    - "attention u net learning where to look for the pancreas"
    - "how many labeled license plates are needed"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"

Connected through hyperedge HE-8 (induced by source paper "fots fast oriented text spotting with a unified network"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "wordsup exploiting word annotations for character based text detection"
    - "east an efficient and accurate scene text detector"
    - "single shot text detector with regional attention"
    - "fused text segmentation networks for multi oriented scene text detection"
    - "sequence to label script identification for multilingual ocr"
    - "deep scene text detection with connected component proposals"

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)