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: "optimization of evolutionary neural networks using hybrid learning algorithms"
  Abstract: Evolutionary artificial neural networks (EANNS) refer to a special class of artificial neural networks (ANNs) in which evolution is another fundamental form of adaptation in addition to learning. Evolutionary algorithms are used to adapt the connection weights, network architecture and learning algorithms according to the problem environment. Even though evolutionary algorithms are well known as efficient global search algorithms, very often they miss the best local solutions in the complex solution space. We propose a hybrid meta-heuristic learning approach combining evolutionary learning and local search methods (using 1/sup st/ and 2/sup nd/ order error information) to improve the learning and faster convergence obtained using a direct evolutionary approach. The proposed technique is tested on three different chaotic time series and the test results are compared with some popular neuro-fuzzy systems and a cutting angle method of global optimization. Empirical results reveal that the proposed technique is efficient in spite of the computational complexity.

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"
    - "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 "decision support systems using intelligent paradigms"):
  with members:
    - "neuro fuzzy systems sate of the art modeling techniques"

Connected through hyperedge HE-3 (induced by source paper "meta learning evolutionary artificial neural networks"):
  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)