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: "multi function convolutional neural networks for improving image classification performance"
  Abstract: Traditional Convolutional Neural Networks (CNNs) typically use the same activation function (usually ReLU) for all neurons with non-linear mapping operations. For example, the deep convolutional architecture Inception-v4 uses ReLU. To improve the classification performance of traditional CNNs, a new "Multi-function Convolutional Neural Network" (MCNN) is created by using different activation functions for different neurons. For $n$ neurons and $m$ different activation functions, there are a total of $m^n-m$ MCNNs and only $m$ traditional CNNs. Therefore, the best model is very likely to be chosen from MCNNs because there are $m^n-2m$ more MCNNs than traditional CNNs. For performance analysis, two different datasets for two applications (classifying handwritten digits from the MNIST database and classifying brain MRI images into one of the four stages of Alzheimer's disease (AD)) are used. For both applications, an activation function is randomly selected for each layer of a MCNN. For the AD diagnosis application, MCNNs using a newly created multi-function Inception-v4 architecture are constructed. Overall, simulations show that MCNNs can outperform traditional CNNs in terms of multi-class classification accuracy for both applications. An important future research work will be to efficiently select the best MCNN from $m^n-m$ candidate MCNNs. Current CNN software only provides users with partial functionality of MCNNs since different layers can use different activation functions but not individual neurons in the same layer. Thus, modifying current CNN software systems such as ResNets, DenseNets, and Dual Path Networks by using multiple activation functions and developing more effective and faster MCNN software systems and tools would be very useful to solve difficult practical image classification problems.

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.CV(Computer Vision and Pattern Recognition)