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 "approximating optimization problems using eas on scale free networks"):
  Abstract: It has been experimentally observed that real-world networks follow certain topological properties, such as small-world, power-law etc. To study these networks, many random graph models, such as Preferential Attachment, have been proposed. #R##N#In this paper, we consider the deterministic properties which capture power-law degree distribution and degeneracy. Networks with these properties are known as scale-free networks in the literature. Many interesting problems remain NP-hard on scale-free networks. We study the relationship between scale-free properties and the approximation ratio of some commonly used evolutionary algorithms. #R##N#For the Vertex Cover, we observe experimentally that the (1+1) EA always gives the better result than a greedy local search, even when it runs for only $O(n \log n)$ steps. We give the construction of a scale-free network in which a multi-objective algorithm and a greedy algorithm obtain optimal solutions, while the (1+1) EA obtains the worst possible solution with constant probability. #R##N#We prove that for the Dominating Set, Vertex Cover, Connected Dominating Set and Independent Set, the (1+1) EA obtains constant-factor approximation in expected run time $O(n \log n)$ and $O(n^4)$ respectively. Whereas, GSEMO gives even better approximation than (1+1) EA in expected run time $O(n^3)$ for Dominating Set, Vertex Cover and Connected Dominating Set on such networks.

Includes members:
  Member "one size fits all effectiveness of local search on structured data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an aposteriorical clusterability criterion for k means and simplicity of clustering")
  Member "greed is good for deterministic scale free networks":
    also connected through other hyperedges:
      (none)
  Member "algorithmic complexity of power law networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prsim sublinear time simrank computation on large power law graphs")
  Member "performance analysis of evolutionary algorithms for the minimum label spanning tree problem":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "error analysis of elitist random search heuristics")
  Member "multiplicative drift analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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.DS(Data Structures and Algorithms)