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 "better runtime guarantees via stochastic domination"):
  Abstract: Apart from few exceptions, the mathematical runtime analysis of evolutionary algorithms is mostly concerned with expected runtimes. In this work, we argue that stochastic domination is a notion that should be used more frequently in this area. Stochastic domination allows to formulate much more informative performance guarantees than the expectation alone, it allows to decouple the algorithm analysis into the true algorithmic part of detecting a domination statement and probability theoretic part of deriving the desired probabilistic guarantees from this statement, and it allows simpler and more natural proofs. #R##N#As particular results, we prove a fitness level theorem which shows that the runtime is dominated by a sum of independent geometric random variables, we prove tail bounds for several classic problems, and we give a short and natural proof for Witt's result that the runtime of any $(\mu,p)$ mutation-based algorithm on any function with unique optimum is subdominated by the runtime of a variant of the (1+1) evolutionary algorithm on the OneMax function.

Includes members:
  Member "probabilistic tools for the analysis of randomized optimization heuristics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "working principles of binary differential evolution")
  Member "optimal parameter choices via precise black box analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "bounding bloat in genetic programming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "computational complexity analysis of genetic programming")
  Member "a general dichotomy of evolutionary algorithms on monotone functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interpolating local and global search by controlling the variance of standard bit mutation")
  Member "runtime analysis for self adaptive mutation rates":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "fast genetic algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "the 1 λ evolutionary algorithm with self adjusting mutation rate":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "a tight runtime analysis of the 1 lambda lambda genetic algorithm on onemax":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a general dichotomy of evolutionary algorithms on monotone functions")

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)