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 "citylearn diverse real world environments for sample efficient navigation policy learning"):
  Abstract: Visual navigation tasks in real-world environments often require both self-motion and place recognition feedback. While deep reinforcement learning has shown success in solving these perception and decision-making problems in an end-to-end manner, these algorithms require large amounts of experience to learn navigation policies from high-dimensional data, which is generally impractical for real robots due to sample complexity. In this paper, we address these problems with two main contributions. We first leverage place recognition and deep learning techniques combined with goal destination feedback to generate compact, bimodal image representations that can then be used to effectively learn control policies from a small amount of experience. Second, we present an interactive framework, CityLearn, that enables for the first time training and deployment of navigation algorithms across city-sized, realistic environments with extreme visual appearance changes. CityLearn features more than 10 benchmark datasets, often used in visual place recognition and autonomous driving research, including over 100 recorded traversals across 60 cities around the world. We evaluate our approach on two CityLearn environments, training our navigation policy on a single traversal. Results show our method can be over 2 orders of magnitude faster than when using raw images, and can also generalize across extreme visual changes including day to night and summer to winter transitions.

Includes members:
  Member "a hybrid compact neural architecture for visual place recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "learning to follow directions in street view":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "nuscenes a multimodal dataset for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review deep learning on 3d point clouds")
  Member "visual place recognition for aerial robotics exploring accuracy computation trade off for local image descriptors":
    also connected through other hyperedges:
      (none)
  Member "cross view policy learning for street navigation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "orthogonal gradient descent for continual learning")
  Member "automatic coverage selection for surface based visual localization":
    also connected through other hyperedges:
      (none)
  Member "the streetlearn environment and dataset":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "retouchdown adding touchdown to streetlearn as a shareable resource for language grounding tasks in street view")

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)