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: "a general dichotomy of evolutionary algorithms on monotone functions"
  Abstract: It is known that the evolutionary algorithm $(1+1)$-EA with mutation rate $c/n$ optimises every monotone function efficiently if $c<1$, and needs exponential time on some monotone functions (HotTopic functions) if $c\geq 2.2$. We study the same question for a large variety of algorithms, particularly for $(1+\lambda)$-EA, $(\mu+1)$-EA, $(\mu+1)$-GA, their fast counterparts like fast $(1+1)$-EA, and for $(1+(\lambda,\lambda))$-GA. We find that all considered mutation-based algorithms show a similar dichotomy for HotTopic functions, or even for all monotone functions. For the $(1+(\lambda,\lambda))$-GA, this dichotomy is in the parameter $c\gamma$, which is the expected number of bit flips in an individual after mutation and crossover, neglecting selection. For the fast algorithms, the dichotomy is in $m_2/m_1$, where $m_1$ and $m_2$ are the first and second falling moment of the number of bit flips. Surprisingly, the range of efficient parameters is not affected by either population size $\mu$ nor by the offspring population size $\lambda$. #R##N#The picture changes completely if crossover is allowed. The genetic algorithms $(\mu+1)$-GA and fast $(\mu+1)$-GA are efficient for arbitrary mutations strengths if $\mu$ is large enough.

Connected through hyperedge HE-1 (induced by source paper "interpolating local and global search by controlling the variance of standard bit mutation"):
  with members:
    - "maximizing drift is not optimal for solving onemax"
    - "optimal parameter choices via precise black box analysis"
    - "fast artificial immune systems"
    - "better runtime guarantees via stochastic domination"
    - "towards a theory guided benchmarking suite for discrete black box optimization heuristics profiling 1 λ ea variants on onemax and leadingones"
    - "on the effectiveness of simple success based parameter selection mechanisms for two classical discrete black box optimization benchmark problems"
    - "iohprofiler a benchmarking and profiling tool for iterative optimization heuristics"
    - "fast genetic algorithms"

Connected through hyperedge HE-2 (induced by source paper "offspring population size matters when comparing evolutionary algorithms with self adjusting mutation rates"):
  with members:
    - "self adjusting mutation rates with provably optimal success rules"
    - "optimal parameter choices via precise black box analysis"
    - "complexity theory for discrete black box optimization heuristics"
    - "towards a theory guided benchmarking suite for discrete black box optimization heuristics profiling 1 λ ea variants on onemax and leadingones"
    - "on the effectiveness of simple success based parameter selection mechanisms for two classical discrete black box optimization benchmark problems"
    - "towards a more practice aware runtime analysis of evolutionary algorithms"
    - "adaptive population models for offspring populations and parallel evolutionary algorithms"

Connected through hyperedge HE-3 (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"
    - "destructiveness of lexicographic parsimony pressure and alleviation by a concatenation crossover in genetic programming"
    - "optimizing monotone functions can be difficult"
    - "on the impact of mutation selection balance on the runtime of evolutionary algorithms"

Connected through hyperedge HE-4 (induced by source paper "hyper parameter tuning for the 1 λ λ ga"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "iohprofiler a benchmarking and profiling tool for iterative optimization heuristics"
    - "runtime analysis of the 1 λ λ genetic algorithm on random satisfiable 3 cnf formulas"
    - "hyperband a novel bandit based approach to hyperparameter optimization"

Connected through hyperedge HE-5 (induced by source paper "on the benefits of populations on the exploitation speed of standard steady state genetic algorithms"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "standard steady state genetic algorithms can hillclimb faster than mutation only evolutionary algorithms"
    - "level based analysis of genetic algorithms and other search processes"

Connected through hyperedge HE-6 (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"
    - "fast genetic algorithms"
    - "the 1 λ evolutionary algorithm with self adjusting mutation rate"
    - "ranking based black box complexity"

Connected through hyperedge HE-7 (induced by source paper "better runtime guarantees via stochastic domination"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "optimal parameter choices via precise black box analysis"
    - "bounding bloat in genetic programming"
    - "runtime analysis for self adaptive mutation rates"
    - "fast genetic algorithms"
    - "the 1 λ evolutionary algorithm with self adjusting mutation rate"
    - "a tight runtime analysis of the 1 lambda lambda genetic algorithm on onemax"
    - "adaptive drift analysis"

Connected through hyperedge HE-8 (induced by source paper "a tight runtime analysis for the mu lambda ea"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "better runtime guarantees via stochastic domination"
    - "runtime analysis for self adaptive mutation rates"
    - "the 1 λ evolutionary algorithm with self adjusting mutation rate"
    - "a lower bound analysis of population based evolutionary algorithms for pseudo boolean functions"
    - "tight lower bound on the probability of a binomial exceeding its expectation"
    - "adaptive drift analysis"
    - "multiplicative drift analysis"

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)