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: "on the impact of mutation selection balance on the runtime of evolutionary algorithms"
  Abstract: The interplay between mutation and selection plays a fundamental role in the behaviour of evolutionary algorithms (EAs). However, this interplay is still not completely understood. This paper presents a rigorous runtime analysis of a non-elitist population-based EA that uses the linear ranking selection mechanism. The analysis focuses on how the balance between parameter $\eta$, controlling the selection pressure in linear ranking, and parameter $\chi$ controlling the bit-wise mutation rate, impacts the runtime of the algorithm. The results point out situations where a correct balance between selection pressure and mutation rate is essential for finding the optimal solution in polynomial time. In particular, it is shown that there exist fitness functions which can only be solved in polynomial time if the ratio between parameters $\eta$ and $\chi$ is within a narrow critical interval, and where a small change in this ratio can increase the runtime exponentially. Furthermore, it is shown quantitatively how the appropriate parameter choice depends on the characteristics of the fitness function. In addition to the original results on the runtime of EAs, this paper also introduces a very useful analytical tool, i.e., multi-type branching processes, to the runtime analysis of non-elitist population-based EAs.

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 "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"
    - "optimizing monotone functions can be difficult"

Connected through hyperedge HE-3 (induced by source paper "runtime analysis for self adaptive mutation rates"):
  with members:
    - "theory of parameter control for discrete black box optimization provable performance gains through dynamic parameter choices"
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "drift theory in continuous search spaces expected hitting time of the 1 1 es with 1 5 success rule"
    - "optimal parameter choices via precise black box analysis"
    - "better runtime guarantees via stochastic domination"
    - "the 1 λ evolutionary algorithm with self adjusting mutation rate"
    - "self adaptation of mutation rates in non elitist populations"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"

Connected through hyperedge HE-4 (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"
    - "a general dichotomy of evolutionary algorithms on monotone functions"
    - "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"

Connected through hyperedge HE-5 (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"
    - "a general dichotomy of evolutionary algorithms on monotone functions"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "self adaptation of mutation rates in non elitist populations"):
  with members:
    - "solving problems with unknown solution length at almost no extra cost"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "level based analysis of genetic algorithms and other search processes"

Connected through hyperedge HE-7 (induced by source paper "a lower bound analysis of population based evolutionary algorithms for pseudo boolean functions"):
  with members:
    - "a large population size can be unhelpful in evolutionary algorithms"
    - "a new method for lower bounds on the running time of evolutionary algorithms"

Connected through hyperedge HE-8 (induced by source paper "the quasispecies regime for the simple genetic algorithm with roulette wheel selection"):
  with members:
    - "level based analysis of genetic algorithms and other search processes"
    - "critical control of a genetic algorithm"

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)