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: "exact markov chain based runtime analysis of a discrete particle swarm optimization algorithm on sorting and onemax"
  Abstract: Meta-heuristics are powerful tools for solving optimization problems whose structural properties are unknown or cannot be exploited algorithmically. We propose such a meta-heuristic for a large class of optimization problems over discrete domains based on the particle swarm optimization (PSO) paradigm. Our goal is to provide a comprehensive formal analysis of the performance of this algorithm on certain "easy" reference problems in a black-box setting, namely the problem of sorting items by transpositions and the problem OneMax, which asks to maximize the number of ones occurring in a bitstring. We provide a Markov-model of the proposed algorithm and obtain from it upper and lower bounds on the expected number of function evaluations needed by the algorithm to solve the reference problems. Our bounds are essentially tight with respect to the Markov-model. For a suitable choice of the algorithm parameters the expected optimization time of the proposed algorithm is comparable to known results for an evolutionary algorithm. Furthermore, for other parameter regimes, the algorithm behaves less greedy and more explorative, which can be desirable in order to escape local optima. Our analysis provides insight on how much runtime we pay for this. In order to obtain our results, we introduce an indistinguishability property for the states of a Markov chain and provide bounds on the solution of a recurrence equation with non-constant coefficients by integration. Implicitly, we extend in this work the well-known fitness level method for randomized search heuristics to a non-elitist setting.

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.DM(Discrete Mathematics)