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 "informative path planning and mapping for active sensing under localization uncertainty"):
  Abstract: Robotic platforms are emerging as a timely and cost-efficient tool for exploration and monitoring. However, an open challenge is planning missions for robust, efficient data acquisition in complex environments. To address this issue, we introduce an informative planning framework for active sensing scenarios that accounts for the robot pose uncertainty. Our strategy exploits a Gaussian Process model to capture a target environmental field given the uncertainty on its inputs. This allows us to maintain robust maps, which are used for planning information-rich trajectories in continuous space. A key aspect of our method is a new utility function that couples the localization and field mapping objectives, enabling us to trade-off exploration against exploitation in a principled way. Extensive simulations show that our approach outperforms existing strategies, with reductions of up to 45.1% and 6.3% in mean pose uncertainty and map error. We demonstrate a proof of concept in an indoor temperature mapping scenario.

Includes members:
  Member "bayesian optimisation for safe navigation under localisation uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bayesian optimisation under uncertain inputs")
  Member "sampling based incremental information gathering with applications to robotic exploration and environmental monitoring":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "active learning for uav based semantic mapping")
  Member "an informative path planning framework for uav based terrain monitoring":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "building an aerial ground robotics system for precision farming")
  Member "multiresolution mapping and informative path planning for uav based terrain monitoring":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "building an aerial ground robotics system for precision farming")

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)