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: "ranking based black box complexity"
  Abstract: Randomized search heuristics such as evolutionary algorithms, simulated annealing, and ant colony optimization are a broadly used class of general-purpose algorithms. Analyzing them via classical methods of theoretical computer science is a growing field. While several strong runtime analysis results have appeared in the last 20 years, a powerful complexity theory for such algorithms is yet to be developed. We enrich the existing notions of black-box complexity by the additional restriction that not the actual objective values, but only the relative quality of the previously evaluated solutions may be taken into account by the black-box algorithm. Many randomized search heuristics belong to this class of algorithms.

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"
    - "runtime analysis of the 1 λ λ genetic algorithm on random satisfiable 3 cnf formulas"
    - "the 1 λ evolutionary algorithm with self adjusting mutation rate"
    - "update strength in edas and aco how to avoid genetic drift"

Connected through hyperedge HE-2 (induced by source paper "working principles of binary differential evolution"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "fast re optimization via structural diversity"
    - "upper bounds on the running time of the univariate marginal distribution algorithm on onemax"
    - "better runtime guarantees via stochastic domination"
    - "significance based estimation of distribution algorithms"
    - "level based analysis of the population based incremental learning algorithm"
    - "the query complexity of a permutation based variant of mastermind"
    - "update strength in edas and aco how to avoid genetic drift"

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"
    - "precise runtime analysis for plateaus"
    - "the query complexity of a permutation based variant of mastermind"
    - "drift analysis"
    - "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 "black box complexity of the binary value function"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "runtime analysis of the 1 λ λ genetic algorithm on random satisfiable 3 cnf formulas"
    - "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-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"
    - "precise runtime analysis for plateaus"
    - "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"

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"
    - "runtime analysis of the 1 λ λ genetic algorithm on random satisfiable 3 cnf formulas"
    - "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"
    - "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 "significance based estimation of distribution algorithms"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "precise runtime analysis for plateaus"
    - "improved runtime bounds for the univariate marginal distribution algorithm via anti concentration"
    - "update strength in edas and aco how to avoid genetic drift"
    - "erratum simplified drift analysis for proving lower bounds in evolutionary computation"

Connected through hyperedge HE-8 (induced by source paper "better fixed arity unbiased black box algorithms"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "reducing the arity in unbiased black box complexity"
    - "playing mastermind with constant size memory"
    - "faster black box algorithms through higher arity operators"

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)