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 "local sampling based planning with sequential bayesian updates"):
  Abstract: Sampling-based planners are the predominant motion planning paradigm for robots. Majority of sampling-based planners use a global random sampling scheme to guarantee completeness. However, these schemes are sample inefficient as the majority of the samples are wasted in narrow passages. Consequently, information about the local structure is neglected. Local sampling-based motion planners, on the other hand, take sequential decisions of random walks to samples valid trajectories in configuration space. However, current approaches do not adapt their strategies according to the success and failures of past samples. #R##N#In this work, we introduce a local sampling-based motion planner with a Bayesian update scheme for modelling a sampling proposal distribution. The proposal distribution is sequentially updated based on previous sample outcomes, consequently shaping the proposal distribution according to local obstacles and constraints in the configuration space. Thus, through learning from past observed outcomes, we can maximise the likelihood of sampling in regions that have a higher probability to form trajectories within narrow passages.

Includes members:
  Member "balancing global exploration and local connectivity exploitation with rapidly exploring random disjointed trees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bayesian local sampling based planning")
  Member "sampling based algorithms for optimal motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "incremental sampling based algorithms for optimal motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bayesian local sampling based planning")

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)