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: "runtime analysis for self adaptive mutation rates"
  Abstract: We propose and analyze a self-adaptive version of the $(1,\lambda)$ evolutionary algorithm in which the current mutation rate is part of the individual and thus also subject to mutation. A rigorous runtime analysis on the OneMax benchmark function reveals that a simple local mutation scheme for the rate leads to an expected optimization time (number of fitness evaluations) of $O(n\lambda/\log\lambda+n\log n)$ when $\lambda$ is at least $C \ln n$ for some constant $C > 0$. For all values of $\lambda \ge C \ln n$, this performance is asymptotically best possible among all $\lambda$-parallel mutation-based unbiased black-box algorithms. #R##N#Our result shows that self-adaptation in evolutionary computation can find complex optimal parameter settings on the fly. At the same time, it proves that a relatively complicated self-adjusting scheme for the mutation rate proposed by Doerr, Gie{\ss}en, Witt, and Yang~(GECCO~2017) can be replaced by our simple endogenous scheme. #R##N#On the technical side, the paper contributes new tools for the analysis of two-dimensional drift processes arising in the analysis of dynamic parameter choices in EAs, including bounds on occupation probabilities in processes with non-constant drift.

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"
    - "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"
    - "self adaptation of mutation rates in non elitist populations"

Connected through hyperedge HE-2 (induced by source paper "self adjusting mutation rates with provably optimal success rules"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "better runtime guarantees via stochastic domination"
    - "on the effectiveness of simple success based parameter selection mechanisms for two classical discrete black box optimization benchmark problems"
    - "hyper heuristics can achieve optimal performance for pseudo boolean optimisation"
    - "adaptive drift analysis"
    - "multiplicative drift analysis"
    - "adaptive population models for offspring populations and parallel evolutionary algorithms"
    - "a new method for lower bounds on the running time of evolutionary algorithms"

Connected through hyperedge HE-3 (induced by source paper "maximizing drift is not optimal for solving onemax"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "towards a more practice aware runtime analysis of evolutionary algorithms"
    - "multiplicative drift analysis"
    - "a new method for lower bounds on the running time of evolutionary algorithms"

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"
    - "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-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"
    - "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"

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 of the 1 λ λ genetic algorithm on random satisfiable 3 cnf formulas"
    - "the right mutation strength for multi valued decision variables"
    - "self adaptation of mutation rates in non elitist populations"
    - "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"

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)