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: "probabilistic planning for continuous dynamic systems under bounded risk"
  Abstract: This paper presents a model-based planner called the Probabilistic Sulu Planner or the p-Sulu Planner, which controls stochastic systems in a goal directed manner within user-specified risk bounds. The objective of the p-Sulu Planner is to allow users to command continuous, stochastic systems, such as unmanned aerial and space vehicles, in a manner that is both intuitive and safe. To this end, we first develop a new plan representation called a chance-constrained qualitative state plan (CCQSP), through which users can specify the desired evolution of the plant state as well as the acceptable level of risk. An example of a CCQSP statement is "go to A through B within 30 minutes, with less than 0.001% probability of failure." We then develop the p-Sulu Planner, which can tractably solve a CCQSP planning problem. In order to enable CCQSP planning, we develop the following two capabilities in this paper: 1) risk-sensitive planning with risk bounds, and 2) goal-directed planning in a continuous domain with temporal constraints. The first capability is to ensures that the probability of failure is bounded. The second capability is essential for the planner to solve problems with a continuous state space such as vehicle path planning. We demonstrate the capabilities of the p-Sulu Planner by simulations on two real-world scenarios: the path planning and scheduling of a personal aerial vehicle as well as the space rendezvous of an autonomous cargo spacecraft.

Connected through hyperedge HE-1 (induced by source paper "a real time approach for chance constrained motion planning with dynamic obstacles"):
  with members:
    - "using uncertainty data in chance constrained trajectory planning"

Connected through hyperedge HE-2 (induced by source paper "risk averse planning under uncertainty"):
  with members:
    - "stochastic finite state control of pomdps with ltl specifications"
    - "safe policy synthesis in multi agent pomdps via discrete time barrier functions"
    - "risk sensitive and robust decision making a cvar optimization approach"
    - "algorithms for cvar optimization in mdps"
    - "solving pomdps by searching in policy space"
    - "sparse stochastic finite state controllers for pomdps"

Connected through hyperedge HE-3 (induced by source paper "fpr fast path risk algorithm to evaluate collision probability"):
  with members:
    - "provably safe robot navigation with obstacle uncertainty"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "learning to search via retrospective imitation"):
  with members:
    - "continuous control with deep reinforcement learning"
    - "learning to search better than your teacher"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "search based structured prediction"
    - "optimal solutions for multi unit combinatorial auctions branch and bound heuristics"

Connected through hyperedge HE-5 (induced by source paper "chance constrained motion planning for high dimensional robots"):
  with members:
    - "improving trajectory optimization using a roadmap framework"

Connected through hyperedge HE-6 (induced by source paper "vulcan a monte carlo algorithm for large chance constrained mdps with risk bounding functions"):
  with members:
    - "risk sensitive reinforcement learning applied to control under constraints"

Connected through hyperedge HE-7 (induced by source paper "efficient motion planning for problems lacking optimal substructure"):
  with members:
    - "new perspective on sampling based motion planning via random geometric graphs"
    - "risk constrained reinforcement learning with percentile risk criteria"
    - "a note on the unsolvability of the weighted region shortest path problem"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-8 (induced by source paper "real time stochastic optimal control for multi agent quadrotor systems"):
  with members:
    - "gpu based path integral control with learned dynamics"
    - "latent kullback leibler control for continuous state systems using probabilistic graphical models"
    - "outdoor flocking and formation flight with autonomous aerial robots"
    - "path integral control by reproducing kernel hilbert space embedding"
    - "path integral policy improvement with covariance matrix adaptation"

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.AI(Artificial Intelligence)