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: "segmentation free vehicle license plate recognition using convnet rnn"
  Abstract: While vehicle license plate recognition (VLPR) is usually done with a sliding window approach, it can have limited performance on datasets with characters that are of variable width. This can be solved by hand-crafting algorithms to prescale the characters. While this approach can work fairly well, the recognizer is only aware of the pixels within each detector window, and fails to account for other contextual information that might be present in other parts of the image. A sliding window approach also requires training data in the form of presegmented characters, which can be more difficult to obtain. In this paper, we propose a unified ConvNet-RNN model to recognize real-world captured license plate photographs. By using a Convolutional Neural Network (ConvNet) to perform feature extraction and using a Recurrent Neural Network (RNN) for sequencing, we address the problem of sliding window approaches being unable to access the context of the entire image by feeding the entire image as input to the ConvNet. This has the added benefit of being able to perform end-to-end training of the entire model on labelled, full license plate images. Experimental results comparing the ConvNet-RNN architecture to a sliding window-based approach shows that the ConvNet-RNN architecture performs significantly better.

Connected through hyperedge HE-1 (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"
    - "self adversarial training for human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "lprnet license plate recognition via deep neural networks"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "an opencl tm deep learning accelerator on arria 10"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "reading car license plates using deep convolutional neural networks and lstms"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "spatial transformer networks"
    - "parsenet looking wider to see better"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "license plate detection and recognition using deeply learned convolutional neural networks"):
  with members:
    - "dager deep age gender and emotion recognition using convolutional neural network"
    - "view independent vehicle make model and color recognition using convolutional neural network"
    - "reading car license plates using deep convolutional neural networks and lstms"

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)