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: "building better nurse scheduling algorithms"
  Abstract: The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem with an integer programming formulation and evolutionary algorithms. Secondly, to detail a novel statistical method of comparing and hence building better scheduling algorithms by identifying successful algorithm modifications. The comparison method captures the results of algorithms in a single figure that can then be compared using traditional statistical techniques. Thus, the proposed method of comparing algorithms is an objective procedure designed to assist in the process of improving an algorithm. This is achieved even when some results are non-numeric or missing due to infeasibility. The final algorithm outperforms all previous evolutionary algorithms, which relied on human expertise for modification.

Connected through hyperedge HE-1 (induced by source paper "solving nurse scheduling problem using constraint programming technique"):
  with members:
    - "exploiting problem structure in a genetic algorithm approach to a nurse rostering problem"

Connected through hyperedge HE-2 (induced by source paper "an evolutionary squeaky wheel optimisation approach to personnel scheduling"):
  with members:
    - "an estimation of distribution algorithm with intelligent local search for rule based nurse rostering"
    - "an indirect genetic algorithm for a nurse scheduling problem"
    - "exploiting problem structure in a genetic algorithm approach to a nurse rostering problem"

Connected through hyperedge HE-3 (induced by source paper "a component based heuristic search method with evolutionary eliminations"):
  with members:
    - "an estimation of distribution algorithm for nurse scheduling"
    - "an indirect genetic algorithm for a nurse scheduling problem"
    - "exploiting problem structure in a genetic algorithm approach to a nurse rostering problem"

Connected through hyperedge HE-4 (induced by source paper "an estimation of distribution algorithm for nurse scheduling"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "enhanced direct and indirect genetic algorithm approaches for a mall layout and tenant selection problem"
    - "an indirect genetic algorithm for a nurse scheduling problem"
    - "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-5 (induced by source paper "an estimation of distribution algorithm with intelligent local search for rule based nurse rostering"):
  with members:
    - "parallel problem solving from nature ppsn viii"
    - "an indirect genetic algorithm for a nurse scheduling problem"
    - "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-6 (induced by source paper "improved squeaky wheel optimisation for driver scheduling"):
  with members:
    - "an estimation of distribution algorithm for nurse scheduling"
    - "an indirect genetic algorithm for a nurse scheduling problem"
    - "an indirect genetic algorithm for set covering problems"
    - "exploiting problem structure in a genetic algorithm approach to a nurse rostering problem"
    - "squeaky wheel optimization"

Connected through hyperedge HE-7 (induced by source paper "a component based heuristic search method with adaptive perturbations for hospital personnel scheduling"):
  with members:
    - "an estimation of distribution algorithm for nurse scheduling"
    - "parallel problem solving from nature ppsn viii"
    - "an indirect genetic algorithm for a nurse scheduling problem"
    - "exploiting problem structure in a genetic algorithm approach to a nurse rostering problem"

Connected through hyperedge HE-8 (induced by source paper "a bayesian optimization algorithm for nurse scheduling"):
  with members:
    - "an indirect genetic algorithm for a nurse scheduling problem"
    - "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)