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: "risk averse ω regular markov decision process control"
  Abstract: Many control problems in environments that can be modeled as Markov decision processes (MDPs) concern infinite-time horizon specifications. The classical aim in this context is to compute a control policy that maximizes the probability of satisfying the specification. In many scenarios, there is however a non-zero probability of failure in every step of the system's execution. For infinite-time horizon specifications, this implies that the specification is violated with probability 1 in the long run no matter what policy is chosen, which prevents previous policy computation methods from being useful in these scenarios. #R##N#In this paper, we introduce a new optimization criterion for MDP policies that captures the task of working towards the satisfaction of some infinite-time horizon $\omega$-regular specification. The new criterion is applicable to MDPs in which the violation of the specification cannot be avoided in the long run. We give an algorithm to compute policies that are optimal in this criterion and show that it captures the ideas of optimism and risk-averseness in MDP control: while the computed policies are optimistic in that a MDP run enters a failure state relatively late, they are risk-averse by always maximizing the probability to reach their respective next goal state. We give results on two robot control scenarios to validate the usability of risk-averse MDP policies.

Connected through hyperedge HE-1 (induced by source paper "probabilistic motion planning under temporal tasks and soft constraints"):
  with members:
    - "multigain a controller synthesis tool for mdps with multiple mean payoff objectives"
    - "pareto efficiency in synthesizing shared autonomy policies with temporal logic constraints"
    - "variations on the stochastic shortest path problem"
    - "percentile queries in multi dimensional markov decision processes"
    - "a survey of multi objective sequential decision making"
    - "meet your expectations with guarantees beyond worst case synthesis in quantitative games"
    - "pareto curves for probabilistic model checking"
    - "control of noisy differential drive vehicles from time bounded temporal logic specifications"

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.SY(Systems and Control)