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 runtime analyses of evolutionary algorithms for the euclidean traveling salesperson problem"
  Abstract: Parameterized runtime analysis seeks to understand the influence of problem structure on algorithmic runtime. In this paper, we contribute to the theoretical understanding of evolutionary algorithms and carry out a parameterized analysis of evolutionary algorithms for the Euclidean traveling salesperson problem (Euclidean TSP). #R##N#We investigate the structural properties in TSP instances that influence the optimization process of evolutionary algorithms and use this information to bound the runtime of simple evolutionary algorithms. Our analysis studies the runtime in dependence of the number of inner points $k$ and shows that $(\mu + \lambda)$ evolutionary algorithms solve the Euclidean TSP in expected time $O((\mu/\lambda) \cdot n^3\gamma(\epsilon) + n\gamma(\epsilon) + (\mu/\lambda) \cdot n^{4k}(2k-1)!)$ where $\gamma$ is a function of the minimum angle $\epsilon$ between any three points. #R##N#Finally, our analysis provides insights into designing a mutation operator that improves the upper bound on expected runtime. We show that a mixed mutation strategy that incorporates both 2-opt moves and permutation jumps results in an upper bound of $O((\mu/\lambda) \cdot n^3\gamma(\epsilon) + n\gamma(\epsilon) + (\mu/\lambda) \cdot n^{2k}(k-1)!)$ for the $(\mu+\lambda)$ EA.

Connected through hyperedge HE-1 (induced by source paper "parameterized complexity analysis of randomized search heuristics"):
  with members:
    - "parameterized analysis of multi objective evolutionary algorithms and the weighted vertex cover problem"
    - "multiplicative drift analysis"
    - "evolutionary algorithms and dynamic programming"

Connected through hyperedge HE-2 (induced by source paper "stochastic runtime analysis of a cross entropy algorithm for traveling salesman problems"):
  with members:
    - "update strength in edas and aco how to avoid genetic drift"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-3 (induced by source paper "an analysis of a simple local search algorithm for graph colouring"):
  with members:
    - "a new method for lower bounds on the running time of evolutionary algorithms"

Connected through hyperedge HE-4 (induced by source paper "on the runtime of randomized local search and simple evolutionary algorithms for dynamic makespan scheduling"):
  with members:
    - "multiplicative drift analysis"

Connected through hyperedge HE-5 (induced by source paper "the sampling and learning framework a statistical view of evolutionary algorithms"):
  with members:
    - "performance analysis of evolutionary algorithms for the minimum label spanning tree problem"
    - "multiplicative drift analysis"
    - "more effective crossover operators for the all pairs shortest path problem"
    - "on the approximation ability of evolutionary optimization with application to minimum set cover"
    - "on the complexity of trial and error"
    - "a new method for lower bounds on the running time of evolutionary algorithms"
    - "multi view active learning in the non realizable case"

Connected through hyperedge HE-6 (induced by source paper "performance analysis of evolutionary algorithms for the minimum label spanning tree problem"):
  with members:
    - "a large population size can be unhelpful in evolutionary algorithms"
    - "on the approximation ability of evolutionary optimization with application to minimum set cover"

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)