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: "benchmark for license plate character segmentation"
  Abstract: Automatic License Plate Recognition (ALPR) has been the focus of many researches in the past years. In general, ALPR is divided into the following problems: detection of on-track vehicles, license plates detection, segmention of license plate characters and optical character recognition (OCR). Even though commercial solutions are available for controlled acquisition conditions, e.g., the entrance of a parking lot, ALPR is still an open problem when dealing with data acquired from uncontrolled environments, such as roads and highways when relying only on imaging sensors. Due to the multiple orientations and scales of the license plates captured by the camera, a very challenging task of the ALPR is the License Plate Character Segmentation (LPCS) step, which effectiveness is required to be (near) optimal to achieve a high recognition rate by the OCR. To tackle the LPCS problem, this work proposes a novel benchmark composed of a dataset designed to focus specifically on the character segmentation step of the ALPR within an evaluation protocol. Furthermore, we propose the Jaccard-Centroid coefficient, a new evaluation measure more suitable than the Jaccard coefficient regarding the location of the bounding box within the ground-truth annotation. The dataset is composed of 2,000 Brazilian license plates consisting of 14,000 alphanumeric symbols and their corresponding bounding box annotations. We also present a new straightforward approach to perform LPCS efficiently. Finally, we provide an experimental evaluation for the dataset based on four LPCS approaches and demonstrate the importance of character segmentation for achieving an accurate OCR.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "convolutional neural networks for automatic meter reading"):
  with members:
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "a robust real time automatic license plate recognition based on the yolo detector"):
  with members:
    - "license plate detection and recognition using deeply learned convolutional neural networks"
    - "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"
    - "a large scale car dataset for fine grained categorization and verification"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-4 (induced by source paper "car monitoring system in apartment garages by small autonomous car using deep learning"):
  with members:
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "end to end learning for self driving cars"
    - "yolo9000 better faster stronger"
    - "imagenet large scale visual recognition challenge"

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)