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: "optimal control of mdps with temporal logic constraints"
  Abstract: In this paper, we focus on formal synthesis of control policies for finite Markov decision processes with non-negative real-valued costs. We develop an algorithm to automatically generate a policy that guarantees the satisfaction of a correctness specification expressed as a formula of Linear Temporal Logic, while at the same time minimizing the expected average cost between two consecutive satisfactions of a desired property. The existing solutions to this problem are sub-optimal. By leveraging ideas from automata-based model checking and game theory, we provide an optimal solution. We demonstrate the approach on an illustrative example.

Connected through hyperedge HE-1 (induced by source paper "stochastic finite state control of pomdps with ltl specifications"):
  with members:
    - "counterexample guided strategy improvement for pomdps using recurrent neural networks"
    - "the partially observable games we play for cyber deception"
    - "bounded policy synthesis for pomdps with safe reachability objectives"
    - "qualitative analysis of partially observable markov decision processes"

Connected through hyperedge HE-2 (induced by source paper "certified reinforcement learning with logic guidance"):
  with members:
    - "the probabilistic model checker storm"
    - "deepsynth program synthesis for automatic task segmentation in deep reinforcement learning"
    - "rethinking the discount factor in reinforcement learning a decision theoretic approach"
    - "verifiably safe off model reinforcement learning"
    - "logically constrained reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "from game theoretic multi agent log linear learning to reinforcement learning"
    - "observe and look further achieving consistent performance on atari"

Connected through hyperedge HE-3 (induced by source paper "logically constrained reinforcement learning"):
  with members:
    - "value iteration for long run average reward in markov decision processes"
    - "safe reinforcement learning via shielding"
    - "asynchronous methods for deep reinforcement learning"
    - "reinforcement learning with temporal logic rewards"
    - "correct by synthesis reinforcement learning with temporal logic constraints"
    - "safety constrained reinforcement learning for mdps"
    - "verification of markov decision processes using learning algorithms"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"

Connected through hyperedge HE-4 (induced by source paper "transfer entropy in mdps with temporal logic specifications"):
  with members:
    - "a learning based optimal human robot collaboration with linear temporal logic constraints"
    - "optimal control in markov decision processes via distributed optimization"
    - "information nonanticipative rate distortion function and its applications"
    - "verification of markov decision processes using learning algorithms"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-5 (induced by source paper "tableaux for policy synthesis for mdps with pctl constraints"):
  with members:
    - "a traditional tree style tableau for ltl"
    - "a new rule for ltl tableaux"

Connected through hyperedge HE-6 (induced by source paper "a learning based optimal human robot collaboration with linear temporal logic constraints"):
  with members:
    - "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-7 (induced by source paper "control with probabilistic signal temporal logic"):
  with members:
    - "what is decidable about partially observable markov decision processes with omega regular objectives"

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.LO(Logic in Computer Science)