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: "scalable recommendation from web usage mining using method of moments"
  Abstract: With the advent of mass-available Internet, twenty-first century observed a steady growth in web based commercial services and technology companies. Most of them are based on web applications that receive huge amount of user traffics, and generate massive amount of web usage data containing user-item interactions. We attempt to build a recommendation algorithm based on such web usage data. It is essential that recommendation algorithms for such applications are highly scalable in nature. Existing algorithms such as matrix factorization run several iterations through the dataset, and therefore may not be suitable for large web-scale datasets. Here we propose a highly scalable recommendation algorithm based on recently proposed Method of Moments (also known as Spectral Method). Our method takes only two to three passes through the entire dataset to extract the model parameters during the training phase. We demonstrate the competitive performance of our algorithm in comparison with the existing algorithms on various publicly available datasets through several empirical measures.

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)