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: "neural networks for handwritten english alphabet recognition"
  Abstract: Here, the goal of a character recognition system is to transform a hand written text document on paper into a digital format that can be manipulated by word processor software. The system is required to identify a given input character form by mapping it to a single character in a given character set. Each hand written character is split into a number of segments (depending on the complexity of the alphabet involved) and each segment is handled by a set of purpose built neural network. The final output is unified via a lookup table. Neural network architecture is designed for different values of the network parameters like the number of layers, number of neurons in each layer, the initial values of weights, the training coefficient and the tolerance of the correctness. The optimal selection of these network parameters certainly depends on the complexity of the alphabet.

Connected through hyperedge HE-1 (induced by source paper "non correlated character recognition using artificial neural network"):
  with members:
    - "time efficient approach to offline hand written character recognition using associative memory net"
    - "handwritten bangla alphabet recognition using an mlp based classifier"

Connected through hyperedge HE-2 (induced by source paper "english character recognition using artificial neural network"):
  with members:
    - "handwritten bangla alphabet recognition using an mlp based classifier"

Connected through hyperedge HE-3 (induced by source paper "time efficient approach to offline hand written character recognition using associative memory net"):
  with members:
    - "handwritten bangla alphabet recognition using an mlp based classifier"

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)