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: "evonf a framework for optimization of fuzzy inference systems using neural network learning and evolutionary computation"
  Abstract: Several adaptation techniques have been investigated to optimize fuzzy inference systems. Neural network learning algorithms have been used to determine the parameters of fuzzy inference system. Such models are often called integrated neuro-fuzzy models. In an integrated neuro-fuzzy model there is no guarantee that the neural network learning algorithm will converge and the tuning of fuzzy inference system will be successful. Success of evolutionary search procedures for the optimization of fuzzy inference system is well proven and established in many application areas. In this paper, we explore how the optimization of fuzzy inference systems can be further improved using a meta-heuristic approach combining neural network learning and evolutionary computation. The proposed technique can be considered as a methodology to integrate neural networks, fuzzy inference systems and evolutionary search procedures. We present the theoretical frameworks and some experimental results to demonstrate the efficiency of the proposed technique.

Connected through hyperedge HE-1 (induced by source paper "intelligent systems architectures and perspectives"):
  with members:
    - "neuro fuzzy systems sate of the art modeling techniques"
    - "meta learning evolutionary artificial neural networks"
    - "optimization of evolutionary neural networks using hybrid learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "business intelligence from web usage mining"):
  with members:
    - "neuro fuzzy systems sate of the art modeling techniques"
    - "web mining research a survey"

Connected through hyperedge HE-3 (induced by source paper "export behaviour modeling using evonf approach"):
  with members:
    - "neuro fuzzy systems sate of the art modeling techniques"

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)