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: "exploiting problem structure in a genetic algorithm approach to a nurse rostering problem"
  Abstract: There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problems. In order to overcome this, successful implementations frequently make use of problem specific knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a major UK hospital. The structure of the constraints is used as the basis for a co-evolutionary strategy using co-operating sub-populations. Problem specific knowledge is also used to define a system of incentives and disincentives, and a complementary mutation operator. Empirical results based on 52 weeks of live data show how these features are able to improve an unsuccessful canonical GA to the point where it is able to provide a practical solution to the problem.

Connected through hyperedge HE-1 (induced by source paper "solving nurse scheduling problem using constraint programming technique"):
  with members:
    - "building better nurse scheduling algorithms"

Connected through hyperedge HE-2 (induced by source paper "a flexible integer linear programming formulation for scheduling clinician on call service in hospitals"):
  with members:
    - "improved squeaky wheel optimisation for driver scheduling"

Connected through hyperedge HE-3 (induced by source paper "roster evaluation based on classifiers for the nurse rostering problem"):
  with members:
    - "a market oriented programming environment and its application to distributed multicommodity flow problems"

Connected through hyperedge HE-4 (induced by source paper "team building with answer set programming in the gioia tauro seaport"):
  with members:
    - "the dlv system for knowledge representation and reasoning"

Connected through hyperedge HE-5 (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"
    - "building better nurse scheduling algorithms"

Connected through hyperedge HE-6 (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"
    - "building better nurse scheduling algorithms"

Connected through hyperedge HE-7 (induced by source paper "a bayesian optimisation algorithm for the nurse scheduling problem"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "enhanced direct and indirect genetic algorithm approaches for a mall layout and tenant selection problem"

Connected through hyperedge HE-8 (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"
    - "building better nurse scheduling algorithms"
    - "an indirect genetic algorithm for set covering problems"

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)