human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "parameterized analysis of multi objective evolutionary algorithms and the weighted vertex cover problem"
  Abstract: A rigorous runtime analysis of evolutionary multi-objective optimization for the classical vertex cover problem in the context of parameterized complexity analysis has been presented by Kratsch and Neumann (2013). In this paper, we extend the analysis to the weighted vertex cover problem and provide a fixed parameter evolutionary algorithm with respect to OPT, the cost of the the optimal solution for the problem. Moreover, using a diversity mechanisms, we present a multi-objective evolutionary algorithm that finds a 2-approximation in expected polynomial time and introduce a population-based evolutionary algorithm which finds a $(1+\varepsilon)$-approximation in expected time $O(n\cdot 2^{\min \{n,2(1- \varepsilon)OPT \}} + n^3)$.

Connected through hyperedge HE-1 (induced by source paper "runtime performances of randomized search heuristics for the dynamic weighted vertex cover problem"):
  with members:
    - "on the runtime of randomized local search and simple evolutionary algorithms for dynamic makespan scheduling"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "multiplicative drift analysis"
    - "chernoff hoeffding inequality and applications"

Connected through hyperedge HE-2 (induced by source paper "parameterized complexity analysis of randomized search heuristics"):
  with members:
    - "multiplicative drift analysis"
    - "parameterized runtime analyses of evolutionary algorithms for the euclidean traveling salesperson problem"
    - "evolutionary algorithms and dynamic programming"

Connected through hyperedge HE-3 (induced by source paper "runtime analysis of rls and 1 1 ea for the dynamic weighted vertex cover problem"):
  with members:
    - "the right mutation strength for multi valued decision variables"
    - "on the runtime of randomized local search and simple evolutionary algorithms for dynamic makespan scheduling"

Question: Please tell me which class the center node 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)