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: "virtual blind road following based wearable navigation device for blind people"
  Abstract: To help the blind people walk to the destination efficiently and safely in indoor environment, a novel wearable navigation device is presented in this paper. The locating, way-finding, route following, and obstacle avoiding modules are the essential components in a navigation system, while it remains a challenging task to consider obstacle avoiding during route following, as the indoor environment is complex, changeable, and possibly with dynamic objects. To address this issue, we propose a novel scheme which utilizes a dynamic subgoal selecting strategy to guide the users to the destination and help them bypass obstacles at the same time. This scheme serves as the key component of a complete navigation system deployed on a pair of wearable optical see-through glasses for the ease of use of blind people’s daily walks. The proposed navigation device has been tested on a collection of individuals and proved to be effective on indoor navigation tasks. The sensors embedded are of low cost, small volume, and easy integration, making it possible for the glasses to be widely used as a wearable consumer device.

Connected through hyperedge HE-1 (induced by source paper "wearable travel aid for environment perception and navigation of visually impaired people"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a scene perception system for visually impaired based on object detection and classification using multi modal dcnn"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "smart guiding glasses for visually impaired people in indoor environment"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-2 (induced by source paper "deep learning based robot for automatically picking up garbage on the grass"):
  with members:
    - "smart guiding glasses for visually impaired people in indoor environment"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

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)