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 "autonomous navigation in unknown environments using sparse kernel based occupancy mapping"):
  Abstract: This paper focuses on real-time occupancy mapping and collision checking onboard an autonomous robot navigating in an unknown environment. We propose a new map representation, in which occupied and free space are separated by the decision boundary of a kernel perceptron classifier. We develop an online training algorithm that maintains a very sparse set of support vectors to represent obstacle boundaries in configuration space. We also derive conditions that allow complete (without sampling) collision-checking for piecewise-linear and piecewise-polynomial robot trajectories. We demonstrate the effectiveness of our mapping and collision checking algorithms for autonomous navigation of an Ackermann-drive robot in unknown environments.

Includes members:
  Member "learning based proxy collision detection for robot motion planning applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural collision clearance estimator for fast robot motion planning")
  Member "safe motion planning in unknown environments optimality benchmarks and tractable policies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an efficient reachability based framework for provably safe autonomous navigation in unknown environments")
  Member "warped gaussian processes occupancy mapping with uncertain inputs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stochastic triangular mesh mapping a terrain mapping technique for autonomous mobile robots")
  Member "fastron an online learning based model and active learning strategy for proxy collision detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural collision clearance estimator for fast robot motion planning")
  Member "search based motion planning for quadrotors using linear quadratic minimum time control":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast and safe path following control using a state dependent directional metric")
  Member "voxblox incremental 3d euclidean signed distance fields for on board mav planning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")

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)