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: "hyperplane separation technique for multidimensional mean payoff games"
  Abstract: We consider both finite-state game graphs and recursive game graphs (or pushdown game graphs), that can model the control flow of sequential programs with recursion, with multi-dimensional mean-payoff objectives. In pushdown games two types of strategies are relevant: global strategies, that depend on the entire global history; and modular strategies, that have only local memory and thus do not depend on the context of invocation. We present solutions to several fundamental algorithmic questions and our main contributions are as follows: (1) We show that finite-state multi-dimensional mean-payoff games can be solved in polynomial time if the number of dimensions and the maximal absolute value of the weight is fixed; whereas if the number of dimensions is arbitrary, then problem is already known to be coNP-complete. (2) We show that pushdown graphs with multi-dimensional mean-payoff objectives can be solved in polynomial time. (3) For pushdown games under global strategies both single and multi-dimensional mean-payoff objectives problems are known to be undecidable, and we show that under modular strategies the multi-dimensional problem is also undecidable (whereas under modular strategies the single dimensional problem is NP-complete). We show that if the number of modules, the number of exits, and the maximal absolute value of the weight is fixed, then pushdown games under modular strategies with single dimensional mean-payoff objectives can be solved in polynomial time, and if either of the number of exits or the number of modules is not bounded, then the problem is NP-hard. (4) Finally we show that a fixed parameter tractable algorithm for finite-state multi-dimensional mean-payoff games or pushdown games under modular strategies with single-dimensional mean-payoff objectives would imply the solution of the long-standing open problem of fixed parameter tractability of parity games.

Connected through hyperedge HE-1 (induced by source paper "deciding fast termination for probabilistic vass with nondeterminism"):
  with members:
    - "reachability in vector addition systems is primitive recursive in fixed dimension"
    - "the reachability problem for petri nets is not elementary"
    - "efficient algorithms for asymptotic bounds on termination time in vass"
    - "perfect half space games"
    - "automated recurrence analysis for almost linear expected runtime bounds"
    - "bounded expectations resource analysis for probabilistic programs"
    - "non polynomial worst case analysis of recursive programs"
    - "complexity hierarchies beyond elementary"

Connected through hyperedge HE-2 (induced by source paper "efficient algorithms for asymptotic bounds on termination time in vass"):
  with members:
    - "perfect half space games"
    - "complexity hierarchies beyond elementary"
    - "liveness of parameterized timed networks"
    - "demystifying reachability in vector addition systems"
    - "fixed dimensional energy games are in pseudo polynomial time"
    - "a simple and scalable static analysis for bound analysis and amortized complexity analysis"
    - "the complexity of multi mean payoff and multi energy games"
    - "generalized mean payoff and energy games"

Connected through hyperedge HE-3 (induced by source paper "perfect information stochastic games with generalized mean payoff objectives"):
  with members:
    - "unifying two views on multiple mean payoff objectives in markov decision processes"
    - "multidimensional beyond worst case and almost sure problems for mean payoff objectives"
    - "fixed dimensional energy games are in pseudo polynomial time"
    - "two player perfect information shift invariant submixing stochastic games are half positional"
    - "markov decision processes with multiple long run averageobjectives"
    - "a framework for automated competitive analysis of on line scheduling of firm deadline tasks"
    - "synthesis from ltl specifications with mean payoff objectives"
    - "the complexity of multi mean payoff and multi energy games"

Connected through hyperedge HE-4 (induced by source paper "fixed dimensional energy games are in pseudo polynomial time"):
  with members:
    - "infinite state energy games"
    - "solving parity games on integer vectors"
    - "efficient controller synthesis for consumption games with multiple resource types"
    - "strategy synthesis for multi dimensional quantitative objectives"
    - "reachability games on extended vector addition systems with states"
    - "generalized mean payoff and energy games"

Connected through hyperedge HE-5 (induced by source paper "multidimensional beyond worst case and almost sure problems for mean payoff objectives"):
  with members:
    - "unifying two views on multiple mean payoff objectives in markov decision processes"
    - "percentile queries in multi dimensional markov decision processes"
    - "markov decision processes with multiple long run averageobjectives"
    - "meet your expectations with guarantees beyond worst case synthesis in quantitative games"
    - "the complexity of multi mean payoff and multi energy games"

Connected through hyperedge HE-6 (induced by source paper "robust multidimensional mean payoff games are undecidable"):
  with members:
    - "synthesis from ltl specifications with mean payoff objectives"
    - "efficient controller synthesis for consumption games with multiple resource types"
    - "the complexity of multi mean payoff and multi energy games"
    - "finite memory strategy synthesis for robust multidimensional mean payoff objectives"
    - "strategy synthesis for multi dimensional quantitative objectives"
    - "mean payoff pushdown games"
    - "the complexity of mean payoff automaton expression"
    - "mean payoff automaton expressions"

Connected through hyperedge HE-7 (induced by source paper "infinite state energy games"):
  with members:
    - "solving parity games on integer vectors"
    - "simulation over one counter nets is pspace complete"
    - "strategy synthesis for multi dimensional quantitative objectives"
    - "mean payoff pushdown games"
    - "reachability games on extended vector addition systems with states"
    - "generalized mean payoff and energy games"
    - "energy parity games"

Connected through hyperedge HE-8 (induced by source paper "the complexity of multi mean payoff and multi energy games"):
  with members:
    - "a framework for automated competitive analysis of on line scheduling of firm deadline tasks"
    - "polynomial time algorithms for energy games with special weight structures"
    - "looking at mean payoff and total payoff through windows"
    - "efficient controller synthesis for consumption games with multiple resource types"
    - "strategy synthesis for multi dimensional quantitative objectives"
    - "reachability games on extended vector addition systems with states"
    - "mean payoff automaton expressions"

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.GT(Computer Science and Game Theory)