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: "threshold constraints with guarantees for parity objectives in markov decision processes"
  Abstract: The beyond worst-case synthesis problem was introduced recently by Bruy\`ere et al. [BFRR14]: it aims at building system controllers that provide strict worst-case performance guarantees against an antagonistic environment while ensuring higher expected performance against a stochastic model of the environment. Our work extends the framework of [BFRR14] and follow-up papers, which focused on quantitative objectives, by addressing the case of $\omega$-regular conditions encoded as parity objectives, a natural way to represent functional requirements of systems. #R##N#We build strategies that satisfy a main parity objective on all plays, while ensuring a secondary one with sufficient probability. This setting raises new challenges in comparison to quantitative objectives, as one cannot easily mix different strategies without endangering the functional properties of the system. We establish that, for all variants of this problem, deciding the existence of a strategy lies in ${\sf NP} \cap {\sf coNP}$, the same complexity class as classical parity games. Hence, our framework provides additional modeling power while staying in the same complexity class. #R##N#[BFRR14] V\'eronique Bruy\`ere, Emmanuel Filiot, Mickael Randour, and Jean-Fran\c{c}ois Raskin. Meet your expectations with guarantees: Beyond worst-case synthesis in quantitative games. In Ernst W. Mayr and Natacha Portier, editors, 31st International Symposium on Theoretical Aspects of Computer Science, STACS 2014, March 5-8, 2014, Lyon, France, volume 25 of LIPIcs, pages 199-213. Schloss Dagstuhl - Leibniz - Zentrum fuer Informatik, 2014.

Connected through hyperedge HE-1 (induced by source paper "life is random time is not markov decision processes with window objectives"):
  with members:
    - "multi weighted markov decision processes with reachability objectives"
    - "expected window mean payoff"
    - "universal trees grow inside separating automata quasi polynomial lower bounds for parity games"
    - "the complexity of mean payoff games using universal graphs"
    - "extending finite memory determinacy by boolean combination of winning conditions"
    - "value iteration for long run average reward in markov decision processes"
    - "a storm is coming a modern probabilistic model checker"
    - "window parity games an alternative approach toward parity games with time bounds"

Connected through hyperedge HE-2 (induced by source paper "simple strategies in multi objective mdps technical report"):
  with members:
    - "multi weighted markov decision processes with reachability objectives"
    - "markov automata with multiple objectives"
    - "a storm is coming a modern probabilistic model checker"
    - "multi objective approaches to markov decision processes with uncertain transition parameters"
    - "variations on the stochastic shortest path problem"
    - "mixed integer quadratic programming is in np"
    - "percentile queries in multi dimensional markov decision processes"
    - "a survey of multi objective sequential decision making"

Connected through hyperedge HE-3 (induced by source paper "mso nabla is undecidable"):
  with members:
    - "monadic second order logic with path measure quantifier is undecidable"
    - "emptiness of zero automata is decidable"
    - "the mso u theory of n is undecidable"

Connected through hyperedge HE-4 (induced by source paper "multi weighted markov decision processes with reachability objectives"):
  with members:
    - "stochastic shortest paths and weight bounded properties in markov decision processes"
    - "stream differential equations specification formats and solution methods"
    - "maximizing the conditional expected reward for reaching the goal"
    - "unifying two views on multiple mean payoff objectives in markov decision processes"
    - "multidimensional beyond worst case and almost sure problems for mean payoff objectives"
    - "knowledge spaces and the completeness of learning strategies"
    - "variations on the stochastic shortest path problem"
    - "the odds of staying on budget"

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)