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: "how many attackers can selfish defenders catch"
  Abstract: In a distributed system with {\it attacks} and {\it defenses,} both {\it attackers} and {\it defenders} are self-interested entities. We assume a {\it reward-sharing} scheme among {\it interdependent} defenders; each defender wishes to (locally) maximize her own total {\it fair share} to the attackers extinguished due to her involvement (and possibly due to those of others). What is the {\em maximum} amount of protection achievable by a number of such defenders against a number of attackers while the system is in a {\it Nash equilibrium}? As a measure of system protection, we adopt the {\it Defense-Ratio} \cite{MPPS05a}, which provides the expected (inverse) proportion of attackers caught by the defenders. In a {\it Defense-Optimal} Nash equilibrium, the Defense-Ratio is optimized. #R##N#We discover that the possibility of optimizing the Defense-Ratio (in a Nash equilibrium) depends in a subtle way on how the number of defenders compares to two natural graph-theoretic thresholds we identify. In this vein, we obtain, through a combinatorial analysis of Nash equilibria, a collection of trade-off results: #R##N#- When the number of defenders is either sufficiently small or sufficiently large, there are cases where the Defense-Ratio can be optimized. The optimization problem is computationally tractable for a large number of defenders; the problem becomes ${\cal NP}$-complete for a small number of defenders and the intractability is inherited from a previously unconsidered combinatorial problem in {\em Fractional Graph Theory}. #R##N#- Perhaps paradoxically, there is a middle range of values for the number of defenders where optimizing the Defense-Ratio is never possible.

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)