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: "required sample size for learning sparse bayesian networks with many variables"
  Abstract: AbstractLearning joint probability distributions on n random variables requires exponentialsample size in the generic case. Here we consider the case that a temporal (or causal)order of the variables is known and that the (unknown) graph of causal dependencieshas bounded in-degree ∆. Then the joint measure is uniquely determined by theprobabilities of all (2∆ + 1)-tuples. Upper bounds on the sample size required forestimating their probabilities can be given in terms of the VC-dimension of the set ofcorresponding cylinder sets. The sample size grows less than linearly with n. 1 Introduction Learning joint probability measures on a large set of variables is an important taskof statistics. One of the main motivations to estimate joint probabilities is to studystatistical dependencies and independencies between the random variables [6]. In manyapplications the goal is to obtain information on the underlying causal structure thatproduces the statistical correlations. However, the problem of learning causal structurefrom statistical data is in general a deep problem and cannot be solved by statisticalconsiderations alone [6, 3].Here we do not focus on the problem of uncovering the causal structure, we ratheraddress the problem of learning the probability distribution on a large set of variables.In general, the sample size required for estimating an unknown measure on the variablesX

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)