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 "steap simultaneous trajectory estimation and planning"):
  Abstract: We present a unified probabilistic framework for simultaneous trajectory estimation and planning. Estimation and planning problems are usually considered separately, however, within our framework we show that solving them simultaneously can be more accurate and efficient. The key idea is to compute the full continuous-time trajectory from start to goal at each time-step. While the robot traverses the trajectory, the history portion of the trajectory signifies the solution to the estimation problem, and the future portion of the trajectory signifies a solution to the planning problem. Building on recent probabilistic inference approaches to continuous-time localization and mapping and continuous-time motion planning, we solve the joint problem by iteratively recomputing the maximum a posteriori trajectory conditioned on all available sensor data and cost information. Our approach can contend with high-degree-of-freedom trajectory spaces, uncertainty due to limited sensing capabilities, model inaccuracy, the stochastic effect of executing actions, and can find a solution in real-time. We evaluate our framework empirically in both simulation and on a mobile manipulator.

Includes members:
  Member "continuous time gaussian process motion planning via probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multimodal trajectory optimization for motion planning")
  Member "approximately optimal continuous time motion planning and control via probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "riemannian motion policy fusion through learnable lyapunov function reshaping")
  Member "sparse gaussian processes for continuous time trajectory estimation on matrix lie groups":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "trajectory optimization for coordinated human robot collaboration")
  Member "voxblox incremental 3d euclidean signed distance fields for on board mav planning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "non gaussian slap simultaneous localization and planning under non gaussian uncertainty in static and dynamic environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "safe optimal control under parametric uncertainties")
  Member "4d crop monitoring spatio temporal reconstruction for agriculture":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "agricolmap aerial ground collaborative 3d mapping for precision farming")
  Member "simultaneous localization and planning for physical mobile robots via enabling dynamic replanning in belief space":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "perception aware motion planning via multiobjective search on gpus")
  Member "batch nonlinear continuous time trajectory estimation as exactly sparse gaussian process regression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "stochastic optimization for trajectory planning with heteroscedastic gaussian processes")

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)