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: "analyzing the performance of mutation operators to solve the travelling salesman problem"
  Abstract: The genetic algorithm includes some parameters that should be adjusted, so as to get reliable results. Choosing a representation of the problem addressed, an initial population, a method of selection, a crossover operator, mutation operator, the probabilities of crossover and mutation, and the insertion method creates a variant of genetic algorithms. Our work is part of the answer to this perspective to find a solution for this combinatorial problem. What are the best parameters to select for a genetic algorithm that creates a variety efficient to solve the Travelling Salesman Problem (TSP)? In this paper, we present a comparative analysis of different mutation operators, surrounded by a dilated discussion that justifying the relevance of genetic operators chosen to solving the TSP problem.

Connected through hyperedge HE-1 (induced by source paper "novel methods for enhancing the performance of genetic algorithms"):
  with members:
    - "study of some recent crossovers effects on speed and accuracy of genetic algorithm using symmetric travelling salesman problem"
    - "multiprocessor scheduling using parallel genetic algorithm"
    - "a novel crossover operator for genetic algorithms ring crossover"
    - "on the impact of the migration topology on the island model"

Connected through hyperedge HE-2 (induced by source paper "a multiset model of multi species evolution to solve big deceptive problems"):
  with members:
    - "a comparative study of adaptive crossover operators for genetic algorithms to resolve the traveling salesman problem"

Connected through hyperedge HE-3 (induced by source paper "a meta heuristically approach of the spatial assignment problem of human resources in multi sites enterprise"):
  with members:
    - "a comparative study of adaptive crossover operators for genetic algorithms to resolve the traveling salesman problem"
    - "hybridizing psm and rsm operator for solving np complete problems application to travelling salesman problem"

Connected through hyperedge HE-4 (induced by source paper "hybridizing psm and rsm operator for solving np complete problems application to travelling salesman problem"):
  with members:
    - "a comparative study of adaptive crossover operators for genetic algorithms to resolve the traveling salesman problem"

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)