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: "view independent vehicle make model and color recognition using convolutional neural network"
  Abstract: This paper describes the details of Sighthound's fully automated vehicle make, model and color recognition system. The backbone of our system is a deep convolutional neural network that is not only computationally inexpensive, but also provides state-of-the-art results on several competitive benchmarks. Additionally, our deep network is trained on a large dataset of several million images which are labeled through a semi-automated process. Finally we test our system on several public datasets as well as our own internal test dataset. Our results show that we outperform other methods on all benchmarks by significant margins. Our model is available to developers through the Sighthound Cloud API at this https URL

Connected through hyperedge HE-1 (induced by source paper "vehicle shape and color classification using convolutional neural network"):
  with members:
    - "vehicle color recognition using convolutional neural network"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "deep learning based vehicle make model classification"):
  with members:
    - "vehicle classification using transferable deep neural network features"
    - "ssd single shot multibox detector"
    - "deep residual learning for image recognition"
    - "iat image annotation tool manual"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "vehicle classification using resnets localisation and spatially weighted pooling"):
  with members:
    - "deep residual learning for image recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "license plate detection and recognition using deeply learned convolutional neural networks"):
  with members:
    - "segmentation free vehicle license plate recognition using convnet rnn"
    - "dager deep age gender and emotion recognition using convolutional neural network"
    - "reading car license plates using deep convolutional neural networks and lstms"

Connected through hyperedge HE-5 (induced by source paper "dager deep age gender and emotion recognition using convolutional neural network"):
  with members:
    - "an all in one convolutional neural network for face analysis"

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)