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 "complexity theory for discrete black box optimization heuristics"):
  Abstract: A predominant topic in the theory of evolutionary algorithms and, more generally, theory of randomized black-box optimization techniques is running-time analysis. Running-time analysis is aimed at understanding the performance of a given heuristic on a given problem by bounding the number of function evaluations that are needed by the heuristic to identify a solution of a desired quality. As in general algorithms theory, this running-time perspective is most useful when it is complemented by a meaningful complexity theory that studies the limits of algorithmic solutions.

Includes members:
  Member "optimal parameter choices via precise black box analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "runtime analysis of the 1 λ λ genetic algorithm on random satisfiable 3 cnf formulas":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "the 1 1 elitist black box complexity of leadingones":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "benchmarking discrete optimization heuristics with iohprofiler")
  Member "onemax in black box models with several restrictions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the linear hidden subset problem for the 1 1 ea with scheduled and adaptive mutation rates")
  Member "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "runtime performances of randomized search heuristics for the dynamic weighted vertex cover problem")
  Member "ranking based black box complexity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "reducing the arity in unbiased black box complexity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "black box complexity of the binary value function")
  Member "a new method for lower bounds on the running time of evolutionary algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")

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)