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: "provably safe robot navigation with obstacle uncertainty"
  Abstract: As drones and autonomous cars become more widespread it is becoming increasingly important that robots can operate safely under realistic conditions. The noisy information fed into real systems means that robots must use estimates of the environment to plan navigation. Efficiently guaranteeing that the resulting motion plans are safe under these circumstances has proved difficult. We examine how to guarantee that a trajectory or policy is safe with only imperfect observations of the environment. We examine the implications of various mathematical formalisms of safety and arrive at a mathematical notion of safety of a long-term execution, even when conditioned on observational information. We present efficient algorithms that can prove that trajectories or policies are safe with much tighter bounds than in previous work. Notably, the complexity of the environment does not affect our methods ability to evaluate if a trajectory or policy is safe. We then use these safety checking methods to design a safe variant of the RRT planning algorithm.

Connected through hyperedge HE-1 (induced by source paper "non gaussian chance constrained trajectory planning for autonomous vehicles in the presence of uncertain agents"):
  with members:
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "deep kinematic models for physically realistic prediction of vehicle trajectories"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-2 (induced by source paper "human to robot attention transfer for robot execution failure avoidance using stacked neural networks"):
  with members:
    - "multisensor online transfer learning for 3d lidar based human classification with a mobile robot"
    - "social attention modeling attention in human crowds"
    - "stacked attention networks for image question answering"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (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"
    - "efficient probabilistic collision detection for non convex shapes"

Connected through hyperedge HE-4 (induced by source paper "fast certification of collision probability bounds with uncertain convex obstacles"):
  with members:
    - "chance constrained motion planning for high dimensional robots"
    - "efficient probabilistic collision detection for non convex shapes"

Connected through hyperedge HE-5 (induced by source paper "fpr fast path risk algorithm to evaluate collision probability"):
  with members:
    - "on a formal model of safe and scalable self driving cars"
    - "how far are we from solving pedestrian detection"
    - "evaluating trajectory collision probability through adaptive importance sampling for safe motion planning"
    - "probabilistic planning for continuous dynamic systems under bounded risk"

Connected through hyperedge HE-6 (induced by source paper "look before you sweep visibility aware motion planning"):
  with members:
    - "safe motion planning in unknown environments optimality benchmarks and tractable policies"
    - "planning for robotic exploration based on forward simulation"

Connected through hyperedge HE-7 (induced by source paper "lpd net 3d point cloud learning for large scale place recognition and environment analysis"):
  with members:
    - "from coarse to fine robust hierarchical localization at large scale"
    - "so net self organizing network for point cloud analysis"
    - "relational inductive biases deep learning and graph networks"
    - "dynamic graph cnn for learning on point clouds"
    - "know what your neighbors do 3d semantic segmentation of point clouds"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "voxelnet end to end learning for point cloud based 3d object detection"

Connected through hyperedge HE-8 (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"
    - "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"
    - "shape completion enabled robotic grasping"

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)