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: "channel attention with embedding gaussian process a probabilistic methodology"
  Abstract: Channel attention mechanisms, as the key components of some modern convolutional neural networks (CNNs) architectures, have been commonly used in many visual tasks for effective performance improvement. It is able to reinforce the informative channels and to suppress useless channels of feature maps obtained by CNNs. Recently, different attention modules have been proposed, which are implemented in various ways. However, they are mainly based on convolution and pooling operations, which are lack of intuitive and reasonable insights about the principles that they are based on. Moreover, the ways that they improve the performance of the CNNs is not clear either. In this paper, we propose a Gaussian process embedded channel attention (GPCA) module and interpret the channel attention intuitively and reasonably in a probabilistic way. The GPCA module is able to model the correlations from channels which are assumed as beta distributed variables with Gaussian process prior. As the beta distribution is intractably integrated into the end-to-end training of the CNNs, we utilize an appropriate approximation of the beta distribution to make the distribution assumption implemented easily. In this case, the proposed GPCA module can be integrated into the end-to-end training of the CNNs. Experimental results demonstrate that the proposed GPCA module can improve the accuracies of image classification on four widely used datasets.

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.LG(Machine Learning)