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: "black box complexities of combinatorial problems"
  Abstract: Black-box complexity is a complexity theoretic measure for how difficult a problem is to be optimized by a general purpose optimization algorithm. It is thus one of the few means trying to understand which problems are tractable for genetic algorithms and other randomized search heuristics. #R##N#Most previous work on black-box complexity is on artificial test functions. In this paper, we move a step forward and give a detailed analysis for the two combinatorial problems minimum spanning tree and single-source shortest paths. Besides giving interesting bounds for their black-box complexities, our work reveals that the choice of how to model the optimization problem is non-trivial here. This in particular comes true where the search space does not consist of bit strings and where a reasonable definition of unbiasedness has to be agreed on.

Connected through hyperedge HE-1 (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-2 (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"
    - "a tight runtime analysis for the cga on jump functions edas can cross fitness valleys at no extra cost"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "introducing elitist black box models when does elitist selection weaken the performance of evolutionary algorithms"):
  with members:
    - "ranking based black box complexity"
    - "reducing the arity in unbiased black box complexity"
    - "faster black box algorithms through higher arity operators"

Connected through hyperedge HE-4 (induced by source paper "unbiased black box complexities of jump functions"):
  with members:
    - "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"
    - "faster black box algorithms through higher arity operators"

Connected through hyperedge HE-5 (induced by source paper "reducing the arity in unbiased black box complexity"):
  with members:
    - "black box complexity breaking the o n log n barrier of leadingones"
    - "multiplicative drift analysis"
    - "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"

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)