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: "vehicle classification using transferable deep neural network features"
  Abstract: We address vehicle detection on rear view vehicle images captured from a distance along multi-lane highways, and vehicle classification using transferable features from Deep Neural Network. We address the following problems that are specific to our application: how to utilize dash lane markings to assist vehicle detection, what features are useful for classification on vehicle categories, and how to utilize Deep Neural Network when the size of the labelled data is limited. Experiment results suggest our approach outperforms other state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "deep learning based vehicle make model classification"):
  with members:
    - "view independent vehicle make model and color recognition using convolutional neural network"
    - "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-2 (induced by source paper "a multi strength adversarial training method to mitigate adversarial attacks"):
  with members:
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"
    - "fractional max pooling"
    - "intriguing properties of neural networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "image authentication based on neural networks"

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)