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: "funnel libraries for real time robust feedback motion planning"
  Abstract: We consider the problem of generating motion plans for a robot that are guaranteed to succeed despite uncertainty in the environment, parametric model uncertainty, and disturbances. Furthermore, we consider scenarios where these plans must be generated in real-time, because constraints such as obstacles in the environment may not be known until they are perceived (with a noisy sensor) at runtime. Our approach is to pre-compute a library of "funnels" along different maneuvers of the system that the state is guaranteed to remain within (despite bounded disturbances) when the feedback controller corresponding to the maneuver is executed. We leverage powerful computational machinery from convex optimization (sums-of-squares programming in particular) to compute these funnels. The resulting funnel library is then used to sequentially compose motion plans at runtime while ensuring the safety of the robot. A major advantage of the work presented here is that by explicitly taking into account the effect of uncertainty, the robot can evaluate motion plans based on how vulnerable they are to disturbances. #R##N#We demonstrate and validate our method using extensive hardware experiments on a small fixed-wing airplane avoiding obstacles at high speed (~12 mph), along with thorough simulation experiments of ground vehicle and quadrotor models navigating through cluttered environments. To our knowledge, these demonstrations constitute one of the first examples of provably safe and robust control for robotic systems with complex nonlinear dynamics that need to plan in real-time in environments with complex geometric constraints.

Connected through hyperedge HE-1 (induced by source paper "probably approximately correct vision based planning using motion primitives"):
  with members:
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "pytorch an imperative style high performance deep learning library"
    - "pac bayes with backprop"
    - "robot motion planning in learned latent spaces"
    - "universal planning networks"
    - "pac bayes control learning policies that provably generalize to novel environments"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "motion planning networks"

Connected through hyperedge HE-2 (induced by source paper "reactive navigation in partially familiar planar environments using semantic perceptual feedback"):
  with members:
    - "combining optimal control and learning for visual navigation in novel environments"
    - "conceptualization of object compositions using persistent homology"
    - "admissible abstractions for near optimal task and motion planning"
    - "visual memory for robust path following"
    - "yolov3 an incremental improvement"
    - "sampling based methods for factored task and motion planning"
    - "fast autonomous flight in gps denied and cluttered environments"
    - "6 dof object pose from semantic keypoints"

Connected through hyperedge HE-3 (induced by source paper "reachable sets for safe real time manipulator trajectory design"):
  with members:
    - "towards provably not at fault control of autonomous robots in arbitrary dynamic environments"
    - "safe aggressive quadrotor flight via reachability based trajectory design"
    - "bridging the gap between safety and real time performance in receding horizon trajectory design for mobile robots"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "reducing the barrier to entry of complex robotic software a moveit case study"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "robust vision based obstacle avoidance for micro aerial vehicles in dynamic environments"):
  with members:
    - "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"
    - "nanomap fast uncertainty aware proximity queries with lazy search over local 3d data"
    - "robust stereo visual inertial odometry for fast autonomous flight"

Connected through hyperedge HE-5 (induced by source paper "fast and safe path following control using a state dependent directional metric"):
  with members:
    - "search based motion planning for quadrotors using linear quadratic minimum time control"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-6 (induced by source paper "direct nmpc for post stall motion planning with fixed wing uavs"):
  with members:
    - "pampc perception aware model predictive control for quadrotors"
    - "recent developments in aerial robotics a survey and prototypes overview"

Connected through hyperedge HE-7 (induced by source paper "an efficient reachability based framework for provably safe autonomous navigation in unknown environments"):
  with members:
    - "combining optimal control and learning for visual navigation in novel environments"
    - "safe and complete real time planning and exploration in unknown environments"
    - "a lyapunov based approach to safe reinforcement learning"
    - "safe motion planning in unknown environments optimality benchmarks and tractable policies"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "constrained policy optimization"
    - "provably safe and robust drone routing via sequential path planning a case study in san francisco and the bay area"

Connected through hyperedge HE-8 (induced by source paper "local trajectory stabilization for dexterous manipulation via piecewise affine approximations"):
  with members:
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "controller synthesis for discrete time hybrid polynomial systems via occupation measures"
    - "sampling based polytopic trees for approximate optimal control of piecewise affine systems"
    - "learning dexterous in hand manipulation"
    - "in hand manipulation via motion cones"
    - "reactive planar manipulation with convex hybrid mpc"
    - "deep visual foresight for planning robot motion"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"

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)