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: "a tight runtime analysis for the cga on jump functions edas can cross fitness valleys at no extra cost"
  Abstract: We prove that the compact genetic algorithm (cGA) with hypothetical population size [MATH HERE] poly(n) with high probability finds the optimum of any n-dimensional jump function with jump size [MATH HERE] ln n in [MATH HERE] iterations. Since it is known that the cGA with high probability needs at least [MATH HERE] iterations to optimize the unimodal OneMax function, our result shows that the cGA in contrast to most classic evolutionary algorithms here is able to cross mo derate-sized valleys of low fitness at no extra cost.   Our runtime guarantee improves over the recent upper bound O(µn1.5 log n) valid for µ = Ω(n3.5+e) of Hasenohrl and Sutton (GECCO 2018). For the best choice of the hypothetical population size, this result gives a runtime guarantee of O(n5+e), whereas ours gives O(n log n).   We also provide a simple general method based on parallel runs that, under mild conditions, (i) overcomes the need to specify a suitable population size, but gives a performance close to the one stemming from the best-possible population size, and (ii) transforms EDAs with high-probability performance guarantees into EDAs with similar bounds on the expected runtime.

Connected through hyperedge HE-1 (induced by source paper "on the limitations of the univariate marginal distribution algorithm to deception and where bivariate edas might help"):
  with members:
    - "working principles of binary differential evolution"
    - "multiplicative up drift"
    - "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions"
    - "runtime analysis of the univariate marginal distribution algorithm under low selective pressure and prior noise"
    - "significance based estimation of distribution algorithms"
    - "level based analysis of the univariate marginal distribution algorithm"
    - "level based analysis of the population based incremental learning algorithm"
    - "improved runtime bounds for the univariate marginal distribution algorithm via anti concentration"

Connected through hyperedge HE-2 (induced by source paper "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "fast artificial immune systems"
    - "better runtime guarantees via stochastic domination"
    - "theory of estimation of distribution algorithms"
    - "significance based estimation of distribution algorithms"
    - "the query complexity of a permutation based variant of mastermind"
    - "drift analysis"
    - "improved runtime bounds for the univariate marginal distribution algorithm via anti concentration"

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"
    - "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions"
    - "precise runtime analysis for plateaus"
    - "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 "sharp bounds for genetic drift in edas"):
  with members:
    - "on the limitations of the univariate marginal distribution algorithm to deception and where bivariate edas might help"
    - "upper bounds on the running time of the univariate marginal distribution algorithm on onemax"
    - "update strength in edas and aco how to avoid genetic drift"

Connected through hyperedge HE-5 (induced by source paper "precise runtime analysis for plateaus"):
  with members:
    - "theory of parameter control for discrete black box optimization provable performance gains through dynamic parameter choices"
    - "multiplicative up drift"
    - "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions"
    - "sharp bounds on the runtime of the 1 1 ea via drift analysis and analytic combinatorial tools"
    - "optimal parameter choices via precise black box analysis"
    - "fast genetic algorithms"
    - "level based analysis of genetic algorithms and other search processes"
    - "unbiased black box complexities of jump functions"

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)