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: "smart guiding glasses for visually impaired people in indoor environment"
  Abstract: To overcome the travelling difficulty for the visually impaired group, this paper presents a novel ETA (Electronic Travel Aids)-smart guiding device in the shape of a pair of eyeglasses for giving these people guidance efficiently and safely. Different from existing works, a novel multi sensor fusion based obstacle avoiding algorithm is proposed, which utilizes both the depth sensor and ultrasonic sensor to solve the problems of detecting small obstacles, and transparent obstacles, e.g. the French door. For totally blind people, three kinds of auditory cues were developed to inform the direction where they can go ahead. Whereas for weak sighted people, visual enhancement which leverages the AR (Augment Reality) technique and integrates the traversable direction is adopted. The prototype consisting of a pair of display glasses and several low cost sensors is developed, and its efficiency and accuracy were tested by a number of users. The experimental results show that the smart guiding glasses can effectively improve the user's travelling experience in complicated indoor environment. Thus it serves as a consumer device for helping the visually impaired people to travel safely.

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"
    - "virtual blind road following based wearable navigation device for blind people"
    - "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"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-2 (induced by source paper "deep learning based wearable assistive system for visually impaired people"):
  with members:
    - "deepvio self supervised deep learning of monocular visual inertial odometry using 3d geometric constraints"
    - "wearable travel aid for environment perception and navigation of visually impaired people"
    - "a unified framework for mutual improvement of slam and semantic segmentation"
    - "deep global relative networks for end to end 6 dof visual localization and odometry"
    - "learning transferable uav for forest visual perception"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-3 (induced by source paper "deep learning based robot for automatically picking up garbage on the grass"):
  with members:
    - "virtual blind road following based wearable navigation device for blind people"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "virtual blind road following based wearable navigation device for blind people"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-5 (induced by source paper "robust fusion of lidar and wide angle camera data for autonomous mobile robots"):
  with members:
    - "automatic extrinsic calibration for lidar stereo vehicle sensor setups"
    - "free space detection with self supervised and online trained fully convolutional 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.HC(Human-Computer Interaction)