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: "runtime analysis of the 1 λ λ genetic algorithm on random satisfiable 3 cnf formulas"
  Abstract: The (1 + (λ, λ)) genetic algorithm, first proposed at GECCO 2013, showed a surprisingly good performance on some optimization problems. The theoretical analysis so far was restricted to the O ne M ax  test function, where this GA profited from the perfect fitness-distance correlation. In this work, we conduct a rigorous runtime analysis of this GA on random 3-SAT instances in the planted solution model having at least logarithmic average degree, which are known to have a weaker fitness distance correlation.   We prove that this GA with fixed not too large population size again obtains runtimes better than Θ(n log n), which is a lower bound for most evolutionary algorithms on pseudo-Boolean problems with unique optimum. However, the self-adjusting version of the GA risks reaching population sizes at which the intermediate selection of the GA, due to the weaker fitness-distance correlation, is not able to distinguish a profitable offspring from others. We show that this problem can be overcome by equipping the self-adjusting GA with an upper limit for the population size. Apart from sparse instances, this limit can be chosen in a way that the asymptotic performance does not worsen compared to the idealistic O ne M ax  case. Overall, this work shows that the (1 + (λ, λ)) GA can provably have a good performance on combinatorial search and optimization problems also in the presence of a weaker fitness-distance correlation.

Connected through hyperedge HE-1 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "better runtime guarantees via stochastic domination"
    - "runtime analysis for self adaptive mutation rates"
    - "improved runtime bounds for the univariate marginal distribution algorithm via anti concentration"
    - "fast genetic algorithms"
    - "the 1 λ evolutionary algorithm with self adjusting mutation rate"
    - "update strength in edas and aco how to avoid genetic drift"
    - "self adaptation of mutation rates in non elitist populations"

Connected through hyperedge HE-2 (induced by source paper "black box complexity of the binary value function"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "ranking based black box complexity"
    - "reducing the arity in unbiased black box complexity"
    - "more effective crossover operators for the all pairs shortest path problem"
    - "playing mastermind with constant size memory"
    - "faster black box algorithms through higher arity operators"

Connected through hyperedge HE-3 (induced by source paper "hyper parameter tuning for the 1 λ λ ga"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "a general dichotomy of evolutionary algorithms on monotone functions"
    - "iohprofiler a benchmarking and profiling tool for iterative optimization heuristics"
    - "hyperband a novel bandit based approach to hyperparameter optimization"

Connected through hyperedge HE-4 (induced by source paper "the 1 5 th rule with rollbacks on self adjustment of the population size in the 1 λ λ ga"):
  with members:
    - "towards a more practice aware runtime analysis of evolutionary algorithms"

Connected through hyperedge HE-5 (induced by source paper "merging variables one technique of search in pseudo boolean optimization"):
  with members:
    - "encoding cryptographic functions to sat using transalg system"

Connected through hyperedge HE-6 (induced by source paper "complexity theory for discrete black box optimization heuristics"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "the 1 1 elitist black box complexity of leadingones"
    - "onemax in black box models with several restrictions"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "ranking based black box complexity"
    - "reducing the arity in unbiased black box complexity"
    - "a new method for lower bounds on the running time of evolutionary algorithms"
    - "playing mastermind with constant size memory"

Connected through hyperedge HE-7 (induced by source paper "the 1 λ evolutionary algorithm with self adjusting mutation rate"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "runtime analysis for self adaptive mutation rates"
    - "the right mutation strength for multi valued decision variables"
    - "self adaptation of mutation rates in non elitist populations"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "multiplicative drift analysis"
    - "adaptive population models for offspring populations and parallel 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.NE(Neural and Evolutionary Computing)