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: "precise runtime analysis for plateaus"
  Abstract: To gain a better theoretical understanding of how evolutionary algorithms cope with plateaus of constant fitness, we analyze how the $(1 + 1)$~EA optimizes the $n$-dimensional $Plateau_k$ function. This function has a plateau of second-best fitness in a radius of $k$ around the optimum. As optimization algorithm, we regard the $(1 + 1)$~EA using an arbitrary unbiased mutation operator. Denoting by $\alpha$ the random number of bits flipped in an application of this operator and assuming $\Pr[\alpha = 1] = \Omega(1)$, we show the surprising result that for $k \ge 2$ the expected optimization time of this algorithm is \[\frac{n^k}{k!\Pr[1 \le \alpha \le k]}(1 + o(1)),\] that is, the size of the plateau times the expected waiting time for an iteration flipping between $1$ and $k$ bits. Our result implies that the optimal mutation rate for this function is approximately~$k/en$. Our main analysis tool is a combined analysis of the Markov chains on the search point space and on the Hamming level space, an approach that promises to be useful also for other plateau problems.

Connected through hyperedge HE-1 (induced by source paper "frequency fitness assignment making optimization algorithms invariant under bijective transformations of the objective function"):
  with members:
    - "quality diversity through surprise"
    - "evaluating map elites on constrained optimization problems"
    - "towards a more practice aware runtime analysis of evolutionary algorithms"
    - "quality and diversity optimization a unifying modular framework"
    - "illuminating search spaces by mapping elites"
    - "a tabu search path relinking algorithm to solve the job shop scheduling problem"
    - "novelty search in competitive coevolution"
    - "evaluating las vegas algorithms pitfalls and remedies"

Connected through hyperedge HE-2 (induced by source paper "a tight runtime analysis for the cga on jump functions edas can cross fitness valleys at no extra cost"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions"
    - "fast artificial immune systems"
    - "better runtime guarantees via stochastic domination"
    - "theory of estimation of distribution algorithms"
    - "significance based estimation of distribution algorithms"
    - "drift analysis"
    - "fast genetic algorithms"

Connected through hyperedge HE-3 (induced by source paper "the runtime of the compact genetic algorithm on jump functions"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "a tight runtime analysis for the cga on jump functions edas can cross fitness valleys at no extra cost"
    - "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions"
    - "the query complexity of a permutation based variant of mastermind"
    - "drift analysis"
    - "ranking based black box complexity"
    - "multiplicative drift analysis"
    - "a new method for lower bounds on the running time of evolutionary algorithms"

Connected through hyperedge HE-4 (induced by source paper "analysis of baseline evolutionary algorithms for the packing while travelling problem"):
  with members:
    - "a fully polynomial time approximation scheme for packing while traveling"
    - "evolutionary computation for multicomponent problems opportunities and future directions"
    - "the packing while traveling problem"
    - "packing while traveling mixed integer programming for a class of nonlinear knapsack problems"

Connected through hyperedge HE-5 (induced by source paper "optimal parameter choices via precise black box analysis"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "maximizing drift is not optimal for solving onemax"
    - "better runtime guarantees via stochastic domination"
    - "complexity theory for discrete black box optimization heuristics"
    - "a general dichotomy of evolutionary algorithms on monotone functions"
    - "fast genetic algorithms"
    - "the 1 λ evolutionary algorithm with self adjusting mutation rate"
    - "ranking based black box complexity"

Connected through hyperedge HE-6 (induced by source paper "significance based estimation of distribution algorithms"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "improved runtime bounds for the univariate marginal distribution algorithm via anti concentration"
    - "update strength in edas and aco how to avoid genetic drift"
    - "ranking based black box complexity"
    - "erratum simplified drift analysis for proving lower bounds in evolutionary computation"

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.NE(Neural and Evolutionary Computing)