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: "action selection properties in a software simulated agent"
  Abstract: This article analyses the properties of the Internal Behaviour network, an action selection mechanism previously proposed by the authors, with the aid of a simulation developed for such ends. A brief review of the Internal Behaviour network is followed by the explanation of the implementation of the simulation. Then, experiments are presented and discussed analysing the properties of the action selection in the proposed model.

Connected through hyperedge HE-1 (induced by source paper "integration of computational techniques for the modelling of signal transduction"):
  with members:
    - "a model for combination of external and internal stimuli in the action selection of an autonomous agent"
    - "thinking adaptive towards a behaviours virtual laboratory"
    - "modelling intracellular signalling networks using behaviour based systems and the blackboard architecture"

Connected through hyperedge HE-2 (induced by source paper "modelling intracellular signalling networks using behaviour based systems and the blackboard architecture"):
  with members:
    - "a model for combination of external and internal stimuli in the action selection of an autonomous agent"
    - "thinking adaptive towards a behaviours virtual laboratory"

Connected through hyperedge HE-3 (induced by source paper "dynamic adjustment of the motivation degree in an action selection mechanism"):
  with members:
    - "a model for combination of external and internal stimuli in the action selection of an autonomous agent"

Connected through hyperedge HE-4 (induced by source paper "thinking adaptive towards a behaviours virtual laboratory"):
  with members:
    - "a model for combination of external and internal stimuli in the action selection of an autonomous agent"

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.AI(Artificial Intelligence)