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: "neuro fuzzy systems sate of the art modeling techniques"
  Abstract: Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. ANN learns from scratch by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantages of a combination of ANN and FIS are obvious. There are several approaches to integrate ANN and FIS and very often it depends on the application. We broadly classify the integration of ANN and FIS into three categories namely concurrent model, cooperative model and fully fused model. This paper starts with a discussion of the features of each model and generalize the advantages and deficiencies of each model. We further focus the review on the different types of fused neuro-fuzzy systems and citing the advantages and disadvantages of each model.

Connected through hyperedge HE-1 (induced by source paper "intelligent systems architectures and perspectives"):
  with members:
    - "meta learning evolutionary artificial neural networks"
    - "optimization of evolutionary neural networks using hybrid learning algorithms"
    - "evonf a framework for optimization of fuzzy inference systems using neural network learning and evolutionary computation"

Connected through hyperedge HE-2 (induced by source paper "business intelligence from web usage mining"):
  with members:
    - "evonf a framework for optimization of fuzzy inference systems using neural network learning and evolutionary computation"
    - "web mining research a survey"

Connected through hyperedge HE-3 (induced by source paper "dct based texture classification using soft computing approach"):
  with members:
    - "meta learning evolutionary artificial neural networks"

Connected through hyperedge HE-4 (induced by source paper "modeling chaotic behavior of stock indices using intelligent paradigms"):
  with members:
    - "boosting the differences a fast bayesian classifier neural network"

Connected through hyperedge HE-5 (induced by source paper "decision support systems using intelligent paradigms"):
  with members:
    - "optimization of evolutionary neural networks using hybrid learning algorithms"

Connected through hyperedge HE-6 (induced by source paper "export behaviour modeling using evonf approach"):
  with members:
    - "evonf a framework for optimization of fuzzy inference systems using neural network learning and evolutionary computation"

Connected through hyperedge HE-7 (induced by source paper "meta learning evolutionary artificial neural networks"):
  with members:
    - "optimization of evolutionary neural networks using hybrid learning algorithms"

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)