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: "an indirect genetic algorithm for a nurse scheduling problem"
  Abstract: This paper describes a Genetic Algorithms (GAs) approach to a manpower-scheduling problem arising at a major UK hospital. Although GAs have been successfully used for similar problems in the past, they always had to overcome the limitations of the classical GAs paradigm in handling the conflict between objectives and constraints. The approach taken here is to use an indirect coding based on permutations of the nurses, and a heuristic decoder that builds schedules from these permutations. Computational experiments based on 52 weeks of live data are used to evaluate three different decoders with varying levels of intelligence, and four well-known crossover operators. Results are further enhanced by introducing a hybrid crossover operator and by making use of simple bounds to reduce the size of the solution space. The results reveal that the proposed algorithm is able to find high quality solutions and is both faster and more flexible than a recently published Tabu Search approach.

Connected through hyperedge HE-1 (induced by source paper "a greedy double swap heuristic for nurse scheduling"):
  with members:
    - "a bayesian optimisation algorithm for the nurse scheduling 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"
    - "building better nurse scheduling algorithms"
    - "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"
    - "building better nurse scheduling algorithms"
    - "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"
    - "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-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"
    - "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-6 (induced by source paper "improved squeaky wheel optimisation for driver scheduling"):
  with members:
    - "an estimation of distribution algorithm for nurse scheduling"
    - "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"
    - "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"
    - "building better nurse scheduling algorithms"
    - "exploiting problem structure in a genetic algorithm approach to a nurse rostering problem"

Connected through hyperedge HE-8 (induced by source paper "understanding algorithm performance on an oversubscribed scheduling application"):
  with members:
    - "backbone fragility and the local search cost peak"
    - "squeaky wheel optimization"
    - "when gravity fails local search topology"

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)