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: "what you can do with coordinated samples"
  Abstract: Sample coordination, where similar instances have similar samples, was proposed by statisticians four decades ago as a way to maximize overlap in repeated surveys. Coordinated sampling had been since used for summarizing massive data sets. #R##N#The usefulness of a sampling scheme hinges on the scope and accuracy within which queries posed over the original data can be answered from the sample. We aim here to gain a fundamental understanding of the limits and potential of coordination. Our main result is a precise characterization, in terms of simple properties of the estimated function, of queries for which estimators with desirable properties exist. We consider unbiasedness, nonnegativity, finite variance, and bounded estimates. #R##N#Since generally a single estimator can not be optimal (minimize variance simultaneously) for all data, we propose {\em variance competitiveness}, which means that the expectation of the square on any data is not too far from the minimum one possible for the data. Surprisingly perhaps, we show how to construct, for any function for which an unbiased nonnegative estimator exists, a variance competitive estimator.

Connected through hyperedge HE-1 (induced by source paper "count min optimal estimation and tight error bounds using empirical error distributions"):
  with members:
    - "bias aware sketches"
    - "count min log sketch approximately counting with approximate counters"
    - "improved concentration bounds for count sketch"

Connected through hyperedge HE-2 (induced by source paper "efficient and private approximations of distributed databases calculations"):
  with members:
    - "a minimal variance estimator for the cardinality of big data set intersection"
    - "parallel correctness and transferability for conjunctive queries"
    - "when distributed computation is communication expensive"
    - "calibrating data to sensitivity in private data analysis"
    - "coordinated weighted sampling for estimating aggregates over multiple weight assignments"
    - "explicit learning curves for transduction and application to clustering and compression algorithms"

Connected through hyperedge HE-3 (induced by source paper "variance competitiveness for monotone estimation tightening the bounds"):
  with members:
    - "distance queries from sampled data accurate and efficient"
    - "coordinated weighted sampling for estimating aggregates over multiple weight assignments"
    - "leveraging discarded samples for tighter estimation of multiple set aggregates"

Connected through hyperedge HE-4 (induced by source paper "distance queries from sampled data accurate and efficient"):
  with members:
    - "efficient distributed locality sensitive hashing"
    - "get the most out of your sample optimal unbiased estimators using partial information"
    - "coordinated weighted sampling for estimating aggregates over multiple weight assignments"
    - "leveraging discarded samples for tighter estimation of multiple set aggregates"
    - "stream sampling for variance optimal estimation of subset sums"

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)