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 "analysis of baseline evolutionary algorithms for the packing while travelling problem"):
  Abstract: Although the performance of base-line Evolutionary Algorithms (EAs) on linear functions has been studied rigorously, the same theoretical analyses on non-linear objectives are still far behind. In this paper, variations of the Packing While Travelling (PWT), also known as a non-linear knapsack problem, is considered to address this gap. We investigate PWT for two cities with correlated weights and profits using single-objective and multi-objective algorithms. Our results show that RLS finds the optimal solution in $O(n^3)$ expected time while the GSEMO enhanced with a specific selection operator to deal with exponential population size, calculates all the Pareto front solutions in the same expected time. In the case of uniform weights, (1+1)~EA is able to find the optimal solution in expected time $O(n^2\log{(\max\{n,p_{max}\})})$, where $p_{max}$ is the largest profit of the given items. We also validate the theoretical results using practical experiments and present estimation for expected running time according to the experiments.

Includes members:
  Member "precise runtime analysis for plateaus":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "frequency fitness assignment making optimization algorithms invariant under bijective transformations of the objective function")
  Member "a fully polynomial time approximation scheme for packing while traveling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the node weight dependent traveling salesperson problem approximation algorithms and randomized search heuristics")
  Member "evolutionary computation for multicomponent problems opportunities and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the node weight dependent traveling salesperson problem approximation algorithms and randomized search heuristics")
  Member "the packing while traveling problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic multi objective optimization of the travelling thief problem")
  Member "packing while traveling mixed integer programming for a class of nonlinear knapsack problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a fully polynomial time approximation scheme for packing while traveling")

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)