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: "self adaptation of mutation rates in non elitist populations"
  Abstract: The runtime of evolutionary algorithms (EAs) depends critically on their parameter settings, which are often problem-specific. Automated schemes for parameter tuning have been developed to alleviate the high costs of manual parameter tuning. Experimental results indicate that self-adaptation, where parameter settings are encoded in the genomes of individuals, can be effective in continuous optimisation. However, results in discrete optimisation have been less conclusive. Furthermore, a rigorous runtime analysis that explains how self-adaptation can lead to asymptotic speedups has been missing. This paper provides the first such analysis for discrete, population-based EAs. We apply level-based analysis to show how a self-adaptive EA is capable of fine-tuning its mutation rate, leading to exponential speedups over EAs using fixed mutation rates.

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 "runtime analysis of fitness proportionate selection on linear functions"):
  with members:
    - "multiplicative up drift"
    - "fast genetic algorithms"
    - "hitting times of local and global optima in genetic algorithms with very high selection pressure"
    - "populations can be essential in tracking dynamic optima"
    - "level based analysis of genetic algorithms and other search processes"

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"
    - "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-4 (induced by source paper "combating catastrophic forgetting with developmental compression"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "progressive neural networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "a minimal developmental model can increase evolvability in soft robots"
    - "safe mutations for deep and recurrent neural networks through output gradients"
    - "dynamic routing between capsules"
    - "diffusion based neuromodulation can eliminate catastrophic forgetting in simple neural networks"
    - "pathnet evolution channels gradient descent in super neural networks"

Connected through hyperedge HE-5 (induced by source paper "level based analysis of the univariate marginal distribution algorithm"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "theory of estimation of distribution algorithms"
    - "significance based estimation of distribution algorithms"
    - "level based analysis of the population based incremental learning algorithm"
    - "improved runtime bounds for the univariate marginal distribution algorithm via anti concentration"
    - "update strength in edas and aco how to avoid genetic drift"
    - "level based analysis of genetic algorithms and other search processes"

Connected through hyperedge HE-6 (induced by source paper "the 1 λ evolutionary algorithm with self adjusting mutation rate"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "runtime analysis for self adaptive mutation rates"
    - "runtime analysis of the 1 λ λ genetic algorithm on random satisfiable 3 cnf formulas"
    - "the right mutation strength for multi valued decision variables"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "multiplicative drift analysis"
    - "adaptive population models for offspring populations and parallel evolutionary algorithms"

Connected through hyperedge HE-7 (induced by source paper "level based analysis of genetic algorithms and other search processes"):
  with members:
    - "improved runtime bounds for the univariate marginal distribution algorithm via anti concentration"
    - "hitting times of local and global optima in genetic algorithms with very high selection pressure"
    - "update strength in edas and aco how to avoid genetic drift"
    - "populations can be essential in tracking dynamic optima"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "a new method for lower bounds on the running time of evolutionary algorithms"
    - "on the impact of mutation selection balance on the runtime of evolutionary algorithms"

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)