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 set covering problems"
  Abstract: This paper presents a new type of genetic algorithm for the set covering problem. It differs from previous evolutionary approaches first because it is an indirect algorithm, i.e. the actual solutions are found by an external decoder function. The genetic algorithm itself provides this decoder with permutations of the solution variables and other parameters. Second, it will be shown that results can be further improved by adding another indirect optimisation layer. The decoder will not directly seek out low cost solutions but instead aims for good exploitable solutions. These are then post optimised by another hill-climbing algorithm. Although seemingly more complicated, we will show that this three-stage approach has advantages in terms of solution quality, speed and adaptability to new types of problems over more direct approaches. Extensive computational results are presented and compared to the latest evolutionary and other heuristic approaches to the same data instances.

Connected through hyperedge HE-1 (induced by source paper "chemical reaction optimization for the set covering problem"):
  with members:
    - "optimal v2g scheduling of electric vehicles and unit commitment using chemical reaction optimization"
    - "sensor deployment for air pollution monitoring using public transportation system"
    - "real coded chemical reaction optimization with different perturbation functions"
    - "evolutionary artificial neural network based on chemical reaction optimization"
    - "an efficient mean field approach to the set covering 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"
    - "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"
    - "exploiting problem structure in a genetic algorithm approach to a nurse rostering problem"

Connected through hyperedge HE-3 (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"
    - "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 "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"
    - "building better nurse scheduling algorithms"
    - "exploiting problem structure in a genetic algorithm approach to a nurse rostering problem"
    - "squeaky wheel optimization"

Connected through hyperedge HE-5 (induced by source paper "an indirect genetic algorithm for a nurse scheduling problem"):
  with members:
    - "exploiting problem structure in a genetic algorithm approach to a nurse rostering problem"

Connected through hyperedge HE-6 (induced by source paper "building better nurse scheduling algorithms"):
  with members:
    - "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)