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: "monte carlo motion planning for robot trajectory optimization under uncertainty"
  Abstract: This article presents a novel approach, named MCMP (Monte Carlo Motion Planning), to the problem of motion planning under uncertainty, i.e., to the problem of computing a low-cost path that fulfills probabilistic collision avoidance constraints. MCMP estimates the collision probability (CP) of a given path by sampling via Monte Carlo the execution of a reference tracking controller (in this paper we consider LQG). The key algorithmic contribution of this paper is the design of statistical variance-reduction techniques, namely control variates and importance sampling, to make such a sampling procedure amenable to real-time implementation. MCMP applies this CP estimation procedure to motion planning by iteratively (i) computing an (approximately) optimal path for the deterministic version of the problem (here, using the FMT* algorithm), (ii) computing the CP of this path, and (iii) inflating or deflating the obstacles by a common factor depending on whether the CP is higher or lower than a target value. The advantages of MCMP are threefold: (i) asymptotic correctness of CP estimation, as opposed to most current approximations, which, as shown in this paper, can be off by large multiples and hinder the computation of feasible plans; (ii) speed and parallelizability, and (iii) generality, i.e., the approach is applicable to virtually any planning problem provided that a path tracking controller and a notion of distance to obstacles in the configuration space are available. Numerical results illustrate the correctness (in terms of feasibility), efficiency (in terms of path cost), and computational speed of MCMP.

Connected through hyperedge HE-1 (induced by source paper "perception aware motion planning via multiobjective search on gpus"):
  with members:
    - "perception aware path planning"
    - "real time stochastic kinodynamic motion planning via multiobjective search on gpus"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "simultaneous localization and planning for physical mobile robots via enabling dynamic replanning in belief space"

Connected through hyperedge HE-2 (induced by source paper "safe speed control and collision probability estimation under ego pose uncertainty for autonomous vehicle"):
  with members:
    - "evaluating trajectory collision probability through adaptive importance sampling for safe motion planning"

Connected through hyperedge HE-3 (induced by source paper "rigorous agent evaluation an adversarial approach to uncover catastrophic failures"):
  with members:
    - "deepmind control suite"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "towards the first adversarially robust neural network model on mnist"
    - "a lyapunov based approach to safe reinforcement learning"
    - "distributed prioritized experience replay"
    - "distributed distributional deterministic policy gradients"
    - "constructing unrestricted adversarial examples with generative models"

Connected through hyperedge HE-4 (induced by source paper "deep network uncertainty maps for indoor navigation"):
  with members:
    - "hallucinating robots inferring obstacle distances from partial laser measurements"
    - "active neural localization"
    - "provably safe robot navigation with obstacle uncertainty"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "parse geometry from a line monocular depth estimation with partial laser observation"
    - "deep exploration via bootstrapped dqn"
    - "concrete problems in ai safety"

Connected through hyperedge HE-5 (induced by source paper "deep sequential models for sampling based planning"):
  with members:
    - "inference compilation and universal probabilistic programming"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "detecting events and key actors in multi person videos"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "seeing what you re told sentence guided activity recognition in video"
    - "simultaneous object detection tracking and event recognition"

Connected through hyperedge HE-6 (induced by source paper "provably safe robot navigation with obstacle uncertainty"):
  with members:
    - "fast and bounded probabilistic collision detection in dynamic environments for high dof trajectory planning"
    - "sampling based algorithms for optimal motion planning"
    - "submodular approximation sampling based algorithms and lower bounds"

Connected through hyperedge HE-7 (induced by source paper "real time stochastic kinodynamic motion planning via multiobjective search on gpus"):
  with members:
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "optimal sampling based motion planning under differential constraints the drift case with linear affine dynamics"
    - "optimal sampling based motion planning under differential constraints the driftless case"
    - "kinodynamic rrt optimal motion planning for systems with linear differential constraints"

Connected through hyperedge HE-8 (induced by source paper "evaluating trajectory collision probability through adaptive importance sampling for safe motion planning"):
  with members:
    - "fast and bounded probabilistic collision detection in dynamic environments for high dof trajectory planning"
    - "attention and anticipation in fast visual inertial navigation"
    - "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)