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: "extensive large scale study of error in samping based distinct value estimators for databases"
  Abstract: The problem of distinct value estimation has many applications. Being a critical component of query optimizers in databases, it also has high commercial impact. Many distinct value estimators have been proposed, using various statistical approaches. However, characterizing the errors incurred by these estimators is an open problem: existing analytical approaches are not powerful enough, and extensive empirical studies at large scale do not exist. We conduct an extensive large-scale empirical study of 11 distinct value estimators from four different approaches to the problem over families of Zipfian distributions whose parameters model real-world applications. Our study is the first that \emph{scales to the size of a billion-rows} that today's large commercial databases have to operate in. This allows us to characterize the error that is encountered in real-world applications of distinct value estimation. By mining the generated data, we show that estimator error depends on a key latent parameter --- the average uniform class size --- that has not been studied previously. This parameter also allows us to unearth error patterns that were previously unknown. Importantly, ours is the first approach that provides a framework for \emph{visualizing the error patterns} in distinct value estimation, facilitating discussion of this problem in enterprise settings. Our characterization of errors can be used for several problems in distinct value estimation, such as the design of hybrid estimators. This work aims at the practitioner and the researcher alike, and addresses questions frequently asked by both audiences.

Connected through hyperedge HE-1 (induced by source paper "cardinality estimation in a virtualized network device using online machine learning"):
  with members:
    - "cardinality estimation meets good turing"
    - "adam a method for stochastic optimization"

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.DB(Databases)