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 "deep learning based wearable assistive system for visually impaired people"):
  Abstract: In this paper, we propose a deep learning based assistive system to improve the environment perception experience of visually impaired (VI). The system is composed of a wearable terminal equipped with an RGBD camera and an earphone, a powerful processor mainly for deep learning inferences and a smart phone for touch-based interaction. A data-driven learning approach is proposed to predict safe and reliable walkable instructions using RGBD data and the established semantic map. This map is also used to help VI understand their 3D surrounding objects and layout through well-designed touchscreen interactions. The quantitative and qualitative experimental results show that our learning based obstacle avoidance approach achieves excellent results in both indoor and outdoor datasets with low-lying obstacles. Meanwhile, user studies have also been carried out in various scenarios and showed the improvement of VI's environment perception experience with our system.

Includes members:
  Member "deepvio self supervised deep learning of monocular visual inertial odometry using 3d geometric constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "selectfusion a generic framework to selectively learn multisensory fusion")
  Member "wearable travel aid for environment perception and navigation of visually impaired people":
    also connected through other hyperedges:
      (none)
  Member "a unified framework for mutual improvement of slam and semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep direct visual odometry")
  Member "deep global relative networks for end to end 6 dof visual localization and odometry":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "introduction to camera pose estimation with deep learning")
  Member "learning transferable uav for forest visual perception":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improved reinforcement learning through imitation learning pretraining towards image based autonomous driving")
  Member "rednet residual encoder decoder network for indoor rgb d semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "real time fusion network for rgb d semantic segmentation incorporating unexpected obstacle detection for road driving images")
  Member "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "aggressive perception aware navigation using deep optical flow dynamics and pixelmpc")
  Member "smart guiding glasses for visually impaired people in indoor environment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "wearable travel aid for environment perception and navigation of visually impaired people")

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.RO(Robotics)