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: "fast and bounded probabilistic collision detection in dynamic environments for high dof trajectory planning"
  Abstract: We present a novel approach to perform probabilistic collision detection between a high-DOF robot and high-DOF obstacles in dynamic, uncertain environments. In dynamic environments with a high-DOF robot and moving obstacles, our approach efficiently computes accurate collision probability between the robot and obstacles with upper error bounds. Furthermore, we describe a prediction algorithm for future obstacle position and motion that accounts for both spatial and temporal uncertainties. We present a trajectory optimization algorithm for high-DOF robots in dynamic, uncertain environments based on probabilistic collision detection. We highlight motion planning performance in challenging scenarios with robot arms operating in environments with dynamically moving human obstacles.

Connected through hyperedge HE-1 (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"
    - "efficient probabilistic collision detection for non convex shapes"
    - "i planner intention aware motion planning using learning based human motion prediction"

Connected through hyperedge HE-2 (induced by source paper "provably safe robot navigation with obstacle uncertainty"):
  with members:
    - "monte carlo motion planning for robot trajectory optimization under uncertainty"
    - "sampling based algorithms for optimal motion planning"
    - "submodular approximation sampling based algorithms and lower bounds"

Connected through hyperedge HE-3 (induced by source paper "evaluating trajectory collision probability through adaptive importance sampling for safe motion planning"):
  with members:
    - "attention and anticipation in fast visual inertial navigation"
    - "monte carlo motion planning for robot trajectory optimization under uncertainty"
    - "julia a fast dynamic language for technical computing"

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)