human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "standard steady state genetic algorithms can hillclimb faster than mutation only evolutionary algorithms"):
  Abstract: Explaining to what extent the real power of genetic algorithms lies in the ability of crossover to recombine individuals into higher quality solutions is an important problem in evolutionary computation. In this paper we show how the interplay between mutation and crossover can make genetic algorithms hillclimb faster than their mutation-only counterparts. We devise a Markov Chain framework that allows to rigorously prove an upper bound on the runtime of standard steady state genetic algorithms to hillclimb the OneMax function. The bound establishes that the steady-state genetic algorithms are 25% faster than all standard bit mutation-only evolutionary algorithms with static mutation rate up to lower order terms for moderate population sizes. The analysis also suggests that larger populations may be faster than populations of size 2. We present a lower bound for a greedy (2+1) GA that matches the upper bound for populations larger than 2, rigorously proving that 2 individuals cannot outperform larger population sizes under greedy selection and greedy crossover up to lower order terms. In complementary experiments the best population size is greater than 2 and the greedy genetic algorithms are faster than standard ones, further suggesting that the derived lower bound also holds for the standard steady state (2+1) GA.

Includes members:
  Member "fast genetic algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "escaping local optima using crossover with emergent or reinforced diversity":
    also connected through other hyperedges:
      (none)
  Member "the right mutation strength for multi valued decision variables":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the impact of the cutoff time on the performance of algorithm configurators")
  Member "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "runtime performances of randomized search heuristics for the dynamic weighted vertex cover problem")
  Member "playing mastermind with many colors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the advantages of multiple classes for reducing overfitting from test set reuse")
  Member "a new method for lower bounds on the running time of evolutionary algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "playing mastermind with constant size memory":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "black box complexity of the binary value function")

Question: Please tell me which class the source paper 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)