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 "efficient probabilistic collision detection for non gaussian noise distributions"):
  Abstract: We present algorithms to compute tight upper bounds of collision probability between two objects with positional uncertainties, whose error distributions are given in non-Gaussian forms. Our algorithms can efficiently compute the upper bounds of collision probability when the error distributions are given as Truncated Gaussian, weighted samples, and Truncated Gaussian Mixture Model. We create positional error models on static obstacles captured by noisy depth sensors and dynamic obstacles with complex motion models. We highlight the performance of our probabilistic collision detection algorithms under non-Gaussian positional errors for static and dynamic obstacles in simulated scenarios and real-world robot motion planning scenarios with a 7-DOF robot arm. We demonstrate the benefits of our probabilistic collision detection algorithms in the use of motion planning algorithm in terms of planning collision-free trajectories under environments with sensor and motion uncertainties.

Includes members:
  Member "probabilistically safe robot planning with confidence based human predictions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "quantifying hypothesis space misspecification in learning from human robot demonstrations and physical corrections")
  Member "non gaussian slap simultaneous localization and planning under non gaussian uncertainty in static and dynamic environments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "safe optimal control under parametric uncertainties")
  Member "fast and bounded probabilistic collision detection in dynamic environments for high dof trajectory planning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provably safe robot navigation with obstacle uncertainty")
  Member "efficient probabilistic collision detection for non convex shapes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "provably safe trajectory optimization in the presence of uncertain convex obstacles")
  Member "i planner intention aware motion planning using learning based human motion prediction":
    also connected through other hyperedges:
      (none)

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)