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: "scalability and optimisation of a committee of agents using genetic algorithm"
  Abstract: A population of committees of agents that learn by using neural networks is implemented to simulate the stock market. Each committee of agents, which is regarded as a player in a game, is optimised by continually adapting the architecture of the agents using genetic algorithms. The committees of agents buy and sell stocks by following this procedure: (1) obtain the current price of stocks; (2) predict the future price of stocks; (3) and for a given price trade until all the players are mutually satisfied. The trading of stocks is conducted by following these rules: (1) if a player expects an increase in price then it tries to buy the stock; (2) else if it expects a drop in the price, it sells the stock; (3)and the order in which a player participates in the game is random. The proposed procedure is implemented to simulate trading of three stocks, namely, the Dow Jones, the Nasdaq and the S&P 500. A linear relationship between the number of players and agents versus the computational time to run the complete simulation is observed. It is also found that no player has a monopolistic advantage.

Connected through hyperedge HE-1 (induced by source paper "flexibly bounded rationality and marginalization of irrationality theories for decision making"):
  with members:
    - "semi bounded rationality a model for decision making"

Connected through hyperedge HE-2 (induced by source paper "self organisation of evolving agent populations in digital ecosystems"):
  with members:
    - "the computing of digital ecosystems"
    - "complex adaptive digital ecosystems"
    - "the abc of digital business ecosystems"
    - "digital ecosystems self organisation of evolving agent populations"
    - "digital ecosystems stability of evolving agent populations"
    - "biology of applied digital ecosystems"
    - "digital ecosystems optimisation by a distributed intelligence"
    - "digital ecosystems evolving service oriented architectures"

Connected through hyperedge HE-3 (induced by source paper "stability of evolving multiagent systems"):
  with members:
    - "the computing of digital ecosystems"
    - "complex adaptive digital ecosystems"
    - "the abc of digital business ecosystems"
    - "digital ecosystems self organisation of evolving agent populations"
    - "digital ecosystems stability of evolving agent populations"
    - "biology of applied digital ecosystems"

Connected through hyperedge HE-4 (induced by source paper "digital ecosystems stability of evolving agent populations"):
  with members:
    - "biology of applied digital ecosystems"
    - "digital ecosystems optimisation by a distributed intelligence"
    - "digital ecosystems evolving service oriented architectures"
    - "creating a digital ecosystem service oriented architectures with distributed evolutionary computing"

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.MA(Multiagent Systems)