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 "long range neural navigation policies for the real world"):
  Abstract: Learned Neural Network based policies have shown promising results for robot navigation. However, most of these approaches fall short of being used on a real robot due to the extensive simulated training they require. These simulations lack the visuals and dynamics of the real world, which makes it infeasible to deploy on a real robot. We present a novel Neural Net based policy, NavNet, which allows for easy deployment on a real robot. It consists of two sub policies -- a high level policy which can understand real images and perform long range planning expressed in high level commands; a low level policy that can translate the long range plan into low level commands on a specific platform in a safe and robust manner. For every new deployment, the high level policy is trained on an easily obtainable scan of the environment modeling its visuals and layout. We detail the design of such an environment and how one can use it for training a final navigation policy. Further, we demonstrate a learned low-level policy. We deploy the model in a large office building and test it extensively, achieving $0.80$ success rate over long navigation runs and outperforming SLAM-based models in the same settings.

Includes members:
  Member "learning navigation behaviors end to end with autorl":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta learning curiosity algorithms")
  Member "gibson env real world perception for embodied agents":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an exploration of embodied visual exploration")
  Member "semi parametric topological memory for navigation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "building generalizable agents with a realistic and rich 3d environment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning")
  Member "distributed prioritized experience replay":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "learning to navigate in cities without a map":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "learning deployable navigation policies at kilometer scale from a single traversal":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning")
  Member "minos multimodal indoor simulator for navigation in complex environments":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning")

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)