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 "learning your way without map or compass panoramic target driven visual navigation"):
  Abstract: We present a robot navigation system that uses an imitation learning framework to successfully navigate in complex environments. Our framework takes a pre-built 3D scan of a real environment and trains an agent from pre-generated expert trajectories to navigate to any position given a panoramic view of the goal and the current visual input without relying on map, compass, odometry, GPS or relative position of the target at runtime. Our end-to-end trained agent uses RGB and depth (RGBD) information and can handle large environments (up to $1031m^2$) across multiple rooms (up to $40$) and generalizes to unseen targets. We show that when compared to several baselines using deep reinforcement learning and RGBD SLAM, our method (1) requires fewer training examples and less training time, (2) reaches the goal location with higher accuracy, (3) produces better solutions with shorter paths for long-range navigation tasks, and (4) generalizes to unseen environments given an RGBD map of the environment.

Includes members:
  Member "long range indoor navigation with prm rl":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cooperation without coordination hierarchical predictive planning for decentralized multiagent navigation")
  Member "benchmarking classic and learned navigation in complex 3d environments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an exploration of embodied visual exploration")
  Member "habitat a platform for embodied ai research":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "why build an assistant in minecraft")
  Member "learning navigation behaviors end to end with autorl":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning curiosity algorithms")
  Member "gibson env real world perception for embodied agents":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an exploration of embodied visual exploration")
  Member "on evaluation of embodied navigation agents":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an exploration of embodied visual exploration")
  Member "learning to navigate in cities without a map":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "rednet residual encoder decoder network for indoor rgb d semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "real time fusion network for rgb d semantic segmentation incorporating unexpected obstacle detection for road driving images")

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)