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: "a comparative study of adaptive crossover operators for genetic algorithms to resolve the traveling salesman problem"
  Abstract: Genetic algorithm includes some parameters that should be adjusting so that the algorithm can provide positive results. Crossover operators play very important role by constructing competitive Genetic Algorithms (GAs). In this paper, the basic conceptual features and specific characteristics of various crossover operators in the context of the Traveling Salesman Problem (TSP) are discussed. The results of experimental comparison of more than six different crossover operators for the TSP are presented. The experiment results show that OX operator enables to achieve a better solutions than other operators tested.

Connected through hyperedge HE-1 (induced by source paper "improving tsp solutions using ga with a new hybrid mutation based on knowledge and randomness"):
  with members:
    - "on enhancing genetic algorithms using new crossovers"

Connected through hyperedge HE-2 (induced by source paper "a multiset model of multi species evolution to solve big deceptive problems"):
  with members:
    - "analyzing the performance of mutation operators to solve the travelling 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:
    - "hybridizing psm and rsm operator for solving np complete problems application to travelling salesman problem"
    - "analyzing the performance of mutation operators to solve the 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:
    - "analyzing the performance of mutation operators to solve the travelling 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)