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: "an efficient and layout independent automatic license plate recognition system based on the yolo detector"
  Abstract: In this paper, we present an efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the state-of-the-art YOLO object detector that contains a unified approach for license plate (LP) detection and layout classification to improve the recognition results using post-processing rules. The system is conceived by evaluating and optimizing different models with various modifications, aiming at achieving the best speed/accuracy trade-off at each stage. The networks are trained using images from several datasets, with the addition of various data augmentation techniques, so that they are robust under different conditions. The proposed system achieved an average end-to-end recognition rate of 96.8% across eight public datasets (from five different regions) used in the experiments, outperforming both previous works and commercial systems in the ChineseLP, OpenALPR-EU, SSIG-SegPlate and UFPR-ALPR datasets. In the other datasets, the proposed approach achieved competitive results to those attained by the baselines. Our system also achieved impressive frames per second (FPS) rates on a high-end GPU, being able to perform in real time even when there are four vehicles in the scene. An additional contribution is that we manually labeled 38,334 bounding boxes on 6,237 images from public datasets and made the annotations publicly available to the research community.

Connected through hyperedge HE-1 (induced by source paper "can giraffes become birds an evaluation of image to image translation for data generation"):
  with members:
    - "anda a novel data augmentation technique applied to salient object detection"
    - "masking salient object detection a mask region based convolutional neural network analysis for segmentation of salient objects"
    - "semantic aware grad gan for virtual to real urban scene adaption"
    - "instagan instance aware image to image translation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-2 (induced by source paper "deep representations for cross spectral ocular biometrics"):
  with members:
    - "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"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "periocular recognition using cnn features off the shelf"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-3 (induced by source paper "cnn hyperparameter tuning applied to iris liveness detection"):
  with members:
    - "biometric presentation attack detection beyond the visible spectrum"
    - "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"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "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-4 (induced by source paper "anda a novel data augmentation technique applied to salient object detection"):
  with members:
    - "a simple pooling based design for real time salient object detection"
    - "masking salient object detection a mask region based convolutional neural network analysis for segmentation of salient objects"
    - "salient object detection a survey"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "memory efficient deep salient object segmentation networks on gridized superpixels"
    - "a fast and compact saliency score regression network based on fully convolutional network"
    - "deep saliency with encoded low level distance map and high level features"
    - "visual saliency based on multiscale deep features"

Connected through hyperedge HE-5 (induced by source paper "vehicle re identification exploring feature fusion using multi stream convolutional networks"):
  with members:
    - "a robust real time automatic license plate recognition 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"

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)