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 "from visual place recognition to navigation learning sample efficient control policies across diverse real world environments"):
  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 inputs, 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 images representations that can then be used to effectively learn control policies at kilometer scale from a small amount of experience. Second, we present an interactive and realistic framework, called CityLearn, that enables for the first time the training of navigation algorithms across city-sized, real-world environments with extreme environmental changes. CityLearn features over 10 benchmark real-world datasets often used in place recognition research with more than 100 recorded traversals and across 60 cities around the world. We evaluate our approach in two CityLearn environments where our navigation policy is trained using 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 "nuscenes a multimodal dataset for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review deep learning on 3d point clouds")
  Member "multi process fusion visual place recognition using multiple image processing methods":
    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 "impala scalable distributed deep rl with importance weighted actor learner architectures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "touchdown natural language navigation and spatial reasoning in visual street environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "bdd100k a diverse driving video database with scalable annotation tooling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "active neural localization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  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 "learning deployable navigation policies at kilometer scale from a single traversal":
    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)