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: "fast genetic algorithms"
  Abstract: For genetic algorithms using a bit-string representation of length~$n$, the general recommendation is to take $1/n$ as mutation rate. In this work, we discuss whether this is really justified for multimodal functions. Taking jump functions and the $(1+1)$ evolutionary algorithm as the simplest example, we observe that larger mutation rates give significantly better runtimes. For the $\jump_{m,n}$ function, any mutation rate between $2/n$ and $m/n$ leads to a speed-up at least exponential in $m$ compared to the standard choice. #R##N#The asymptotically best runtime, obtained from using the mutation rate $m/n$ and leading to a speed-up super-exponential in $m$, is very sensitive to small changes of the mutation rate. Any deviation by a small $(1 \pm \eps)$ factor leads to a slow-down exponential in $m$. Consequently, any fixed mutation rate gives strongly sub-optimal results for most jump functions. #R##N#Building on this observation, we propose to use a random mutation rate $\alpha/n$, where $\alpha$ is chosen from a power-law distribution. We prove that the $(1+1)$ EA with this heavy-tailed mutation rate optimizes any $\jump_{m,n}$ function in a time that is only a small polynomial (in~$m$) factor above the one stemming from the optimal rate for this $m$. #R##N#Our heavy-tailed mutation operator yields similar speed-ups (over the best known performance guarantees) for the vertex cover problem in bipartite graphs and the matching problem in general graphs. #R##N#Following the example of fast simulated annealing, fast evolution strategies, and fast evolutionary programming, we propose to call genetic algorithms using a heavy-tailed mutation operator \emph{fast genetic 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"
    - "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"
    - "self adaptation of mutation rates in non elitist populations"

Connected through hyperedge HE-2 (induced by source paper "benchmarking discrete optimization heuristics with iohprofiler"):
  with members:
    - "offspring population size matters when comparing evolutionary algorithms with self adjusting mutation rates"
    - "interpolating local and global search by controlling the variance of standard bit mutation"
    - "hyper parameter tuning for the 1 λ λ ga"
    - "optimal parameter choices via precise black box analysis"
    - "better runtime guarantees via stochastic domination"
    - "theory of estimation of distribution algorithms"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "an exponential lower bound for 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"
    - "fast artificial immune systems"
    - "better runtime guarantees via stochastic domination"
    - "theory of estimation of distribution algorithms"
    - "significance based estimation of distribution algorithms"
    - "the query complexity of a permutation based variant of mastermind"
    - "drift analysis"

Connected through hyperedge HE-4 (induced by source paper "a tight runtime analysis for the cga on jump functions edas can cross fitness valleys at no extra cost"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions"
    - "fast artificial immune systems"
    - "better runtime guarantees via stochastic domination"
    - "theory of estimation of distribution algorithms"
    - "precise runtime analysis for plateaus"
    - "significance based estimation of distribution algorithms"
    - "drift analysis"

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

Connected through hyperedge HE-6 (induced by source paper "runtime analysis of fitness proportionate selection on linear functions"):
  with members:
    - "multiplicative up drift"
    - "hitting times of local and global optima in genetic algorithms with very high selection pressure"
    - "self adaptation of mutation rates in non elitist populations"
    - "populations can be essential in tracking dynamic optima"
    - "level based analysis of genetic algorithms and other search processes"

Connected through hyperedge HE-7 (induced by source paper "is perturbation an effective restart strategy"):
  with members:
    - "local optima networks of the quadratic assignment problem"

Connected through hyperedge HE-8 (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"
    - "the 1 λ evolutionary algorithm with self adjusting mutation rate"
    - "ranking based black box complexity"

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)