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: "enhanced direct and indirect genetic algorithm approaches for a mall layout and tenant selection problem"
  Abstract: During our earlier research, it was recognised that in order to be successful with an indirect genetic algorithm approach using a decoder, the decoder has to strike a balance between being an optimiser in its own right and finding feasible solutions. Previously this balance was achieved manually. Here we extend this by presenting an automated approach where the genetic algorithm itself, simultaneously to solving the problem, sets weights to balance the components out. Subsequently we were able to solve a complex and non-linear scheduling problem better than with a standard direct genetic algorithm implementation.

Connected through hyperedge HE-1 (induced by source paper "a bayesian optimisation algorithm for the nurse scheduling problem"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "exploiting problem structure in a genetic algorithm approach to a nurse rostering problem"

Connected through hyperedge HE-2 (induced by source paper "an estimation of distribution algorithm for nurse scheduling"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "an indirect genetic algorithm for a nurse scheduling problem"
    - "building better nurse scheduling algorithms"
    - "an indirect genetic algorithm for set covering problems"
    - "exploiting problem structure in a genetic algorithm approach to a nurse rostering problem"

Connected through hyperedge HE-3 (induced by source paper "parallel problem solving from nature ppsn viii"):
  with members:
    - "an indirect genetic algorithm for a nurse scheduling problem"
    - "building better nurse scheduling algorithms"
    - "exploiting problem structure in a genetic algorithm approach to a nurse rostering 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)