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: "license plate detection and recognition using deeply learned convolutional neural networks"
  Abstract: This work details Sighthounds fully automated license plate detection and recognition system. The core technology of the system is built using a sequence of deep Convolutional Neural Networks (CNNs) interlaced with accurate and efficient algorithms. The CNNs are trained and fine-tuned so that they are robust under different conditions (e.g. variations in pose, lighting, occlusion, etc.) and can work across a variety of license plate templates (e.g. sizes, backgrounds, fonts, etc). For quantitative analysis, we show that our system outperforms the leading license plate detection and recognition technology i.e. ALPR on several benchmarks. Our system is available to developers through the Sighthound Cloud API at this https URL

Connected through hyperedge HE-1 (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"
    - "boosting adversarial attacks with momentum"
    - "synthesizing robust adversarial examples"
    - "yolo9000 better faster stronger"
    - "reading car license plates using deep convolutional neural networks and lstms"
    - "why should i trust you explaining the predictions of any classifier"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-2 (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"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "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-3 (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"
    - "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"
    - "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-4 (induced by source paper "construction of efficient detectors for character information recognition"):
  with members:
    - "optimal and adaptive algorithms for online boosting"

Connected through hyperedge HE-5 (induced by source paper "a robust real time automatic license plate recognition based on the yolo detector"):
  with members:
    - "towards end to end car license plates detection and recognition with deep neural networks"
    - "cnn for license plate motion deblurring"
    - "yolo9000 better faster stronger"
    - "reading car license plates using deep convolutional neural networks and lstms"
    - "spatially supervised recurrent convolutional neural networks for visual object tracking"
    - "benchmark for license plate character segmentation"
    - "a large scale car dataset for fine grained categorization and verification"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-6 (induced by source paper "arbitext arbitrary oriented text detection in unconstrained scene"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "east an efficient and accurate scene text detector"
    - "deformable convolutional networks"
    - "deep direct regression for multi oriented scene text detection"
    - "detecting oriented text in natural images by linking segments"
    - "multi oriented text detection with fully convolutional networks"
    - "ssd single shot multibox detector"

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)