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: "analyzing evolutionary optimization in noisy environments"
  Abstract: Many optimization tasks have to be handled in noisy environments, where we cannot obtain the exact evaluation of a solution but only a noisy one. For noisy optimization tasks, evolutionary algorithms (EAs), a kind of stochastic metaheuristic search algorithm, have been widely and successfully applied. Previous work mainly focuses on empirical studying and designing EAs for noisy optimization, while, the theoretical counterpart has been little investigated. In this paper, we investigate a largely ignored question, i.e., whether an optimization problem will always become harder for EAs in a noisy environment. We prove that the answer is negative, with respect to the measurement of the expected running time. The result implies that, for optimization tasks that have already been quite hard to solve, the noise may not have a negative effect, and the easier a task the more negatively affected by the noise. On a representative problem where the noise has a strong negative effect, we examine two commonly employed mechanisms in EAs dealing with noise, the re-evaluation and the threshold selection strategies. The analysis discloses that the two strategies, however, both are not effective, i.e., they do not make the EA more noise tolerant. We then find that a small modification of the threshold selection allows it to be proven as an effective strategy for dealing with the noise in the problem.

Connected through hyperedge HE-1 (induced by source paper "running time analysis of the 1 1 ea for robust linear optimization"):
  with members:
    - "robust monotone submodular function maximization"
    - "multi objective maximization of monotone submodular functions with cardinality constraint"
    - "robust influence maximization"
    - "on the runtime of randomized local search and simple evolutionary algorithms for dynamic makespan scheduling"
    - "multiplicative drift analysis"

Connected through hyperedge HE-2 (induced by source paper "on the robustness of median sampling in noisy evolutionary optimization"):
  with members:
    - "analysis of noisy evolutionary optimization when sampling fails"
    - "running time analysis of the 1 1 ea for onemax and leadingones under bit wise noise"

Connected through hyperedge HE-3 (induced by source paper "analysing the robustness of evolutionary algorithms to noise refined runtime bounds and an example where noise is beneficial"):
  with members:
    - "analysis of noisy evolutionary optimization when sampling fails"
    - "memetic algorithms beat evolutionary algorithms on the class of hurdle problems"
    - "general drift analysis with tail bounds"
    - "erratum simplified drift analysis for proving lower bounds in evolutionary computation"

Connected through hyperedge HE-4 (induced by source paper "analysis of noisy evolutionary optimization when sampling fails"):
  with members:
    - "running time analysis of the 1 1 ea for onemax and leadingones under bit wise noise"
    - "erratum simplified drift analysis for proving lower bounds in evolutionary computation"
    - "multiplicative drift analysis"

Connected through hyperedge HE-5 (induced by source paper "sorting by swaps with noisy comparisons"):
  with members:
    - "sorting processes with energy constrained comparisons"
    - "populations can be essential in tracking dynamic optima"

Connected through hyperedge HE-6 (induced by source paper "running time analysis of the 1 1 ea for onemax and leadingones under bit wise noise"):
  with members:
    - "level based analysis of genetic algorithms and other search processes"
    - "adaptive drift analysis"
    - "multiplicative drift analysis"
    - "a new method for lower bounds on the running time of evolutionary algorithms"

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.AI(Artificial Intelligence)