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 "running time analysis of the 1 1 ea for robust linear optimization"):
  Abstract: Evolutionary algorithms (EAs) have found many successful real-world applications, where the optimization problems are often subject to a wide range of uncertainties. To understand the practical behaviors of EAs theoretically, there are a series of efforts devoted to analyzing the running time of EAs for optimization under uncertainties. Existing studies mainly focus on noisy and dynamic optimization, while another common type of uncertain optimization, i.e., robust optimization, has been rarely touched. In this paper, we analyze the expected running time of the (1+1)-EA solving robust linear optimization problems (i.e., linear problems under robust scenarios) with a cardinality constraint $k$. Two common robust scenarios, i.e., deletion-robust and worst-case, are considered. Particularly, we derive tight ranges of the robust parameter $d$ or budget $k$ allowing the (1+1)-EA to find an optimal solution in polynomial running time, which disclose the potential of EAs for robust optimization.

Includes members:
  Member "robust monotone submodular function maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a unified framework of robust submodular optimization")
  Member "multi objective maximization of monotone submodular functions with cardinality constraint":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient algorithms for generating provably near optimal cluster descriptors for explainability")
  Member "robust influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mixed strategies for robust optimization of unknown objectives")
  Member "on the runtime of randomized local search and simple evolutionary algorithms for dynamic makespan scheduling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "runtime performances of randomized search heuristics for the dynamic weighted vertex cover problem")
  Member "analyzing evolutionary optimization in noisy environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the robustness of median sampling in noisy evolutionary optimization")
  Member "multiplicative drift analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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.CC(Computational Complexity)