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: "controlling false positives in frequent itemsets mining through the vc dimension"
  Abstract: Frequent itemsets mining is a fundamental primitive in data mining, requiring to identify all itemsets that appear in a fraction at least $\theta$ of the transactional dataset. However, a transactional dataset only represents a sample from the underlying process that generates the data, the understanding of which is the ultimate goal of data mining. In general, the generative process yields transactions according to a probability distribution. Therefore, the output of traditional frequent itemsets mining algorithms can and typically does contain a large number of spurious patterns that only happen to have a support larger than the minimum threshold $\theta$ in the dataset at hand because of the stochasticity inherent in the dataset generation process. In order for the end user to take informed decisions using the mining results, it is necessary that the returned collection only contains \emph{Real Frequent Itemsets (RFI's)}, i.e., itemsets $A$ such that their \emph{real frequency} (the probability that $A$ appear in a transaction) is greater or equal to the the minimum threshold $\theta$. In this work we present an algorithm to extract a collection $C$ of RFI's while keeping the probability that one or more spurious itemsets are included in $C$ to within a user-specified limit. In other words, we present a statistical test for RFI's for which we can guarantee that the \emph{Family-Wise Error Rate} is within the user-specified limits. We use results from statistical learning theory involving the Vapnik-Chervonenkis (VC) dimension of the problem at hand to accomplish this goal. This allows us to achieve, on the same data, much stricter bounds on the probability of a Type I error than what could be done using traditional multiple hypothesis testing corrections. In our experimental evaluation we show empirically that our test has very high statistical power.

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)