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: "mdp optimal control under temporal logic constraints"
  Abstract: In this paper, we develop a method to automatically generate a control policy for a dynamical system modeled as a Markov Decision Process (MDP). The control specification is given as a Linear Temporal Logic (LTL) formula over a set of propositions defined on the states of the MDP. We synthesize a control policy such that the MDP satisfies the given specification almost surely, if such a policy exists. In addition, we designate an "optimizing proposition" to be repeatedly satisfied, and we formulate a novel optimization criterion in terms of minimizing the expected cost in between satisfactions of this proposition. We propose a sufficient condition for a policy to be optimal, and develop a dynamic programming algorithm that synthesizes a policy that is optimal under some conditions, and sub-optimal otherwise. This problem is motivated by robotic applications requiring persistent tasks, such as environmental monitoring or data gathering, to be performed.

Connected through hyperedge HE-1 (induced by source paper "planning with state abstractions for non markovian task specifications"):
  with members:
    - "evaluating compositionality in sentence embeddings"
    - "accurately and efficiently interpreting human robot instructions of varying granularities"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "interpretable apprenticeship learning with temporal logic specifications"
    - "six challenges for neural machine translation"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "constructing abstraction hierarchies using a skill symbol loop"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning for temporal logic control synthesis with probabilistic satisfaction guarantees"):
  with members:
    - "verifiably safe off model reinforcement learning"
    - "logically constrained reinforcement learning"
    - "logically constrained neural fitted q iteration"
    - "omega regular objectives in model free reinforcement learning"
    - "probabilistic motion planning under temporal tasks and soft constraints"
    - "verification of markov decision processes using learning algorithms"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"
    - "probably approximately correct mdp learning and control with temporal logic constraints"

Connected through hyperedge HE-3 (induced by source paper "falsification of cyber physical systems using deep reinforcement learning"):
  with members:
    - "a policy search method for temporal logic specified reinforcement learning tasks"
    - "an active learning approach to the falsification of black box cyber physical systems"
    - "asynchronous methods for deep reinforcement learning"
    - "continuous deep q learning with model based acceleration"
    - "reinforcement learning with temporal logic rewards"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"
    - "on the robustness of temporal properties for stochastic models"

Connected through hyperedge HE-4 (induced by source paper "probabilistic motion planning under temporal tasks and soft constraints"):
  with members:
    - "risk averse ω regular markov decision process control"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "on the structure and computation of random walk times in finite graphs"):
  with members:
    - "optimal temporal logic planning in probabilistic semantic maps"
    - "minimizing convergence error in multi agent systems via leader selection a supermodular optimization approach"
    - "large scale spectral clustering using resistance distance and spielman teng solvers"
    - "a supermodular optimization framework for leader selection under link noise in linear multi agent systems"
    - "opinion dynamics in social networks a local interaction game with stubborn agents"

Connected through hyperedge HE-6 (induced by source paper "norm conflict resolution in stochastic domains"):
  with members:
    - "interpretable apprenticeship learning with temporal logic specifications"
    - "robust satisfaction of temporal logic specifications via reinforcement learning"
    - "qualitative analysis of pomdps with temporal logic specifications for robotics applications"
    - "on graphical modeling of preference and importance"

Connected through hyperedge HE-7 (induced by source paper "optimal control in markov decision processes via distributed optimization"):
  with members:
    - "decision theoretic planning with non markovian rewards"

Connected through hyperedge HE-8 (induced by source paper "optimal control of mdps with temporal logic constraints"):
  with members:
    - "optimal receding horizon control for finite deterministic systems with temporal logic constraints"
    - "attraction based receding horizon path planning with temporal logic constraints"
    - "energy and mean payoff parity markov decision processes"

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)