human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "efficient probabilistic collision detection for non convex shapes"
  Abstract: We present new algorithms to perform fast probabilistic collision queries between convex as well as non-convex objects. Our approach is applicable to general shapes, where one or more objects are represented using Gaussian probability distributions. We present a fast new algorithm for a pair of convex objects, and extend the approach to non-convex models using hierarchical representations. We highlight the performance of our algorithms with various convex and non-convex shapes on complex synthetic benchmarks and trajectory planning benchmarks for a 7-DOF Fetch robot arm.

Connected through hyperedge HE-1 (induced by source paper "provably safe trajectory optimization in the presence of uncertain convex obstacles"):
  with members:
    - "fast certification of collision probability bounds with uncertain convex obstacles"
    - "chance constrained motion planning for high dimensional robots"
    - "improving trajectory optimization using a roadmap framework"
    - "provably safe robot navigation with obstacle uncertainty"

Connected through hyperedge HE-2 (induced by source paper "fast certification of collision probability bounds with uncertain convex obstacles"):
  with members:
    - "chance constrained motion planning for high dimensional robots"
    - "provably safe robot navigation with obstacle uncertainty"

Connected through hyperedge HE-3 (induced by source paper "efficient probabilistic collision detection for non gaussian noise distributions"):
  with members:
    - "probabilistically safe robot planning with confidence based human predictions"
    - "non gaussian slap simultaneous localization and planning under non gaussian uncertainty in static and dynamic environments"
    - "fast and bounded probabilistic collision detection in dynamic environments for high dof trajectory planning"
    - "i planner intention aware motion planning using learning based human motion prediction"

Question: Please tell me which class the center node 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)