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 "scaling local control to large scale topological navigation"):
  Abstract: Visual topological navigation has been revitalized recently thanks to the advancement of deep learning that substantially improves robot perception. However, the scalability and reliability issue remain challenging due to the complexity and ambiguity of real world images and mechanical constraints of real robots. We present an intuitive solution to show that by accurately measuring the capability of a local controller, large-scale visual topological navigation can be achieved while being scalable and robust. Our approach achieves state-of-the-art results in trajectory following and planning in large-scale environments. It also generalizes well to real robots and new environments without retraining or finetuning.

Includes members:
  Member "a behavioral approach to visual navigation with graph localization networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "topological mapping for manhattan like repetitive environments")
  Member "deep visual mpc policy learning for navigation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "badgr an autonomous self supervised learning based navigation system")
  Member "bayesian relational memory for semantic visual navigation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "from seeing to moving a survey on learning for visual indoor navigation vin")
  Member "neural autonomous navigation with riemannian motion policy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "badgr an autonomous self supervised learning based navigation system")
  Member "combining optimal control and learning for visual navigation in novel environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning")
  Member "visual memory for robust path following":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning view and target invariant visual servoing for navigation")
  Member "zero shot visual imitation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "universal value density estimation for imitation learning and goal conditioned reinforcement learning")
  Member "gibson env real world perception for embodied agents":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an exploration of embodied visual exploration")

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)