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: "measuring and synthesizing systems in probabilistic environments"
  Abstract: Often one has a preference order among the different systems that satisfy a given specification. Under a probabilistic assumption about the possible inputs, such a preference order is naturally expressed by a weighted automaton, which assigns to each word a value, such that a system is preferred if it generates a higher expected value. We solve the following optimal-synthesis problem: given an omega-regular specification, a Markov chain that describes the distribution of inputs, and a weighted automaton that measures how well a system satisfies the given specification under the given input assumption, synthesize a system that optimizes the measured value. #R##N#For safety specifications and measures given by mean-payoff automata, the optimal-synthesis problem amounts to finding a strategy in a Markov decision process (MDP) that is optimal for a long-run average reward objective, which can be done in polynomial time. For general omega-regular specifications, the solution rests on a new, polynomial-time algorithm for computing optimal strategies in MDPs with mean-payoff parity objectives. #R##N#Our algorithm generates optimal strategies consisting of two memoryless strategies and a counter. This counter is in general not bounded. To obtain a finite-state system, we show how to construct an \epsilon-optimal strategy with a bounded counter for any \epsilon>0. We also show how to decide in polynomial time if we can construct an optimal finite-state system (i.e., a system without a counter) for a given specification. #R##N#We have implemented our approach in a tool that takes qualitative and quantitative specifications and automatically constructs a system that satisfies the qualitative specification and optimizes the quantitative specification, if such a system exists. We present experimental results showing optimal systems that were generated in this way.

Connected through hyperedge HE-1 (induced by source paper "strategy representation by decision trees with linear classifiers"):
  with members:
    - "strategy representation by decision trees in reactive synthesis"
    - "a storm is coming a modern probabilistic model checker"
    - "an automaton learning approach to solving safety games over infinite graphs"
    - "the second reactive synthesis competition syntcomp 2015"
    - "counterexample explanation by learning small strategies in markov decision processes"
    - "extended aiger format for synthesis"

Connected through hyperedge HE-2 (induced by source paper "quantitative automata under probabilistic semantics"):
  with members:
    - "nested weighted limit average automata of bounded width"
    - "multigain a controller synthesis tool for mdps with multiple mean payoff objectives"
    - "unifying two views on multiple mean payoff objectives in markov decision processes"
    - "nested weighted automata"
    - "markov decision processes with multiple long run averageobjectives"
    - "discounting in ltl"
    - "energy and mean payoff parity markov decision processes"
    - "expressiveness and closure properties for quantitative languages"

Connected through hyperedge HE-3 (induced by source paper "faster algorithms for quantitative verification in constant treewidth graphs"):
  with members:
    - "nested weighted automata"
    - "faster algorithms for markov decision processes with low treewidth"
    - "mean payoff pushdown games"
    - "the complexity of mean payoff automaton expression"
    - "expressiveness and closure properties for quantitative languages"
    - "mean payoff automaton expressions"
    - "better quality in synthesis through quantitative objectives"

Connected through hyperedge HE-4 (induced by source paper "hankel matrices for weighted visibly pushdown automata"):
  with members:
    - "on the complexity of equivalence and minimisation for q weighted automata"
    - "weighted automata and monadic second order logic"
    - "weighted logics for nested words and algebraic formal power series"
    - "probabilistic weighted automata"
    - "a spectral algorithm for learning hidden markov models"
    - "first order and temporal logics for nested words"

Connected through hyperedge HE-5 (induced by source paper "qualitative analysis of concurrent mean payoff games"):
  with members:
    - "the complexity of partial observation stochastic parity games with finite memory strategies"
    - "the value 1 problem under finite memory strategies for concurrent mean payoff games"
    - "what is decidable about partially observable markov decision processes with omega regular objectives"
    - "synthesis from ltl specifications with mean payoff objectives"
    - "decidable problems for probabilistic automata on infinite words"
    - "partial observation stochastic games how to win when belief fails"
    - "qualitative reachability in stochastic bpa games"
    - "the complexity of solving reachability games using value and strategy iteration"

Connected through hyperedge HE-6 (induced by source paper "how to handle assumptions in synthesis"):
  with members:
    - "doomsday equilibria for omega regular games"
    - "the complexity of admissibility in omega regular games"
    - "meet your expectations with guarantees beyond worst case synthesis in quantitative games"
    - "synthesizing robust systems with ratsy"
    - "rapid recovery for systems with scarce faults"
    - "better quality in synthesis through quantitative objectives"
    - "environment assumptions for synthesis"

Connected through hyperedge HE-7 (induced by source paper "probably safe or live"):
  with members:
    - "bounded satisfiability for pctl"
    - "probabilistic modal mu calculus with independent product"
    - "assume guarantee abstraction refinement for probabilistic systems"

Connected through hyperedge HE-8 (induced by source paper "faster algorithms for markov decision processes with low treewidth"):
  with members:
    - "an o n 2 time algorithm for alternating b uchi games"
    - "probabilistic systems with limsup and liminf 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)