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 "generating robust supervision for learning based visual navigation using hamilton jacobi reachability"):
  Abstract: In Bansal et al. (2019), a novel visual navigation framework that combines learning-based and model-based approaches has been proposed. Specifically, a Convolutional Neural Network (CNN) predicts a waypoint that is used by the dynamics model for planning and tracking a trajectory to the waypoint. However, the CNN inevitably makes prediction errors, ultimately leading to collisions, especially when the robot is navigating through cluttered and tight spaces. In this paper, we present a novel Hamilton-Jacobi (HJ) reachability-based method to generate supervision for the CNN for waypoint prediction. By modeling the prediction error of the CNN as disturbances in dynamics, the proposed method generates waypoints that are robust to these disturbances, and consequently to the prediction errors. Moreover, using globally optimal HJ reachability analysis leads to predicting waypoints that are time-efficient and do not exhibit greedy behavior. Through simulations and experiments on a hardware testbed, we demonstrate the advantages of the proposed approach for navigation tasks where the robot needs to navigate through cluttered, narrow indoor environments.

Includes members:
  Member "an efficient reachability based framework for provably safe autonomous navigation in unknown environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "safety considerations in deep control policies with safety barrier certificates under uncertainty")
  Member "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations")
  Member "neural autonomous navigation with riemannian motion policy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning")
  Member "deep drone racing learning agile flight in dynamic environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "chauffeurnet learning to drive by imitating the best and synthesizing the worst":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "driving policy transfer via modularity and abstraction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to move with affordance maps")
  Member "beauty and the beast optimal methods meet learning for drone racing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep drone racing from simulation to reality with domain randomization")

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)