human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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

Includes members:
  Member "segmentation free vehicle license plate recognition using convnet rnn":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "practical license plate recognition in unconstrained surveillance systems with adversarial super resolution")
  Member "dager deep age gender and emotion recognition using convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exposing and correcting the gender bias in image captioning datasets and models")
  Member "view independent vehicle make model and color recognition using convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vehicle shape and color classification using convolutional neural network")
  Member "reading car license plates using deep convolutional neural networks and lstms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a pooling based scene text proposal technique for scene text reading in the wild")

Question: Please tell me which class the source paper 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)