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: "on the variance of subset sum estimation"
  Abstract: For high volume data streams and large data warehouses, sampling is used for efficient approximate answers to aggregate queries over selected subsets. Mathematically, we are dealing with a set of weighted items and want to support queries to arbitrary subset sums. With unit weights, we can compute subset sizes which together with the previous sums provide the subset averages. The question addressed here is which sampling scheme we should use to get the most accurate subset sum estimates. #R##N#We present a simple theorem on the variance of subset sum estimation and use it to prove variance optimality and near-optimality of subset sum estimation with different known sampling schemes. This variance is measured as the average over all subsets of any given size. By optimal we mean there is no set of input weights for which any sampling scheme can have a better average variance. Such powerful results can never be established experimentally. The results of this paper are derived mathematically. For example, we show that appropriately weighted systematic sampling is simultaneously optimal for all subset sizes. More standard schemes such as uniform sampling and probability-proportional-to-size sampling with replacement can be arbitrarily bad. #R##N#Knowing the variance optimality of different sampling schemes can help deciding which sampling scheme to apply in a given context.

Connected through hyperedge HE-1 (induced by source paper "structure aware sampling flexible and accurate summarization"):
  with members:
    - "stream sampling for variance optimal estimation of subset sums"
    - "algorithms for eps approximations of terrains"
    - "deterministic sampling and range counting in geometric data streams"
    - "medians and beyond new aggregation techniques for sensor networks"

Connected through hyperedge HE-2 (induced by source paper "coordinated weighted sampling for estimating aggregates over multiple weight assignments"):
  with members:
    - "leveraging discarded samples for tighter estimation of multiple set aggregates"
    - "stream sampling for variance optimal estimation of subset sums"

Connected through hyperedge HE-3 (induced by source paper "leveraging discarded samples for tighter estimation of multiple set aggregates"):
  with members:
    - "stream sampling for variance optimal estimation of subset sums"

Connected through hyperedge HE-4 (induced by source paper "stream sampling for variance optimal estimation of subset sums"):
  with members:
    - "logarithmic lower bounds in the cell probe model"

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.DS(Data Structures and Algorithms)