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: "optimizing monotone functions can be difficult"
  Abstract: Extending previous analyses on function classes like linear functions, we analyze how the simple (1+1) evolutionary algorithm optimizes pseudo-Boolean functions that are strictly monotone. Contrary to what one would expect, not all of these functions are easy to optimize. The choice of the constant $c$ in the mutation probability $p(n) = c/n$ can make a decisive difference. #R##N#We show that if $c   33$, we present a strictly monotone function such that the (1+1) evolutionary algorithm with overwhelming probability does not find the optimum within $2^{\Omega(n)}$ iterations. This is the first time that we observe that a constant factor change of the mutation probability changes the run-time by more than constant factors.

Connected through hyperedge HE-1 (induced by source paper "exponential slowdown for larger populations the µ 1 ea on monotone functions"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "a general dichotomy of evolutionary algorithms on monotone functions"
    - "destructiveness of lexicographic parsimony pressure and alleviation by a concatenation crossover in genetic programming"
    - "on the impact of mutation selection balance on the runtime of evolutionary algorithms"

Connected through hyperedge HE-2 (induced by source paper "a general dichotomy of evolutionary algorithms on monotone functions"):
  with members:
    - "fast genetic algorithms"
    - "optimal parameter settings for the 1 lambda lambda genetic algorithm"
    - "a tight runtime analysis of the 1 lambda lambda genetic algorithm on onemax"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "matchings with lower quotas algorithms and complexity"
    - "multiplicative drift analysis"

Connected through hyperedge HE-3 (induced by source paper "adaptive drift analysis"):
  with members:
    - "multiplicative drift analysis"

Connected through hyperedge HE-4 (induced by source paper "multiplicative drift analysis"):
  with members:
    - "adaptive drift analysis"
    - "tight bounds on the optimization time of the 1 1 ea on linear functions"

Connected through hyperedge HE-5 (induced by source paper "a new method for lower bounds on the running time of evolutionary algorithms"):
  with members:
    - "multiplicative drift analysis"
    - "adaptive population models for offspring populations and parallel evolutionary algorithms"
    - "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)