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: "an experimental study of distributed quantile estimation"
  Abstract: Quantiles are very important statistics information used to describe the distribution of datasets. Given the quantiles of a dataset, we can easily know the distribution of the dataset, which is a fundamental problem in data analysis. However, quite often, computing quantiles directly is inappropriate due to the memory limitations. Further, in many settings such as data streaming and sensor network model, even the data size is unpredictable. Although the quantiles computation has been widely studied, it was mostly in the sequential setting. In this paper, we study several quantile computation algorithms in the \emph{distributed} setting and compare them in terms of space usage, running time, and accuracy. Moreover, we provide detailed experimental comparisons between several popular algorithms. Our work focuses on the approximate quantile algorithms which provide error bounds. Approximate quantiles have received more attentions than exact ones since they are often faster, can be more easily adapted to the distributed setting while giving sufficiently good statistical information on the data sets.

Connected through hyperedge HE-1 (induced by source paper "moment based quantile sketches for efficient high cardinality aggregation queries"):
  with members:
    - "an efficient bandit algorithm for realtime multivariate optimization"
    - "macrobase prioritizing attention in fast data"
    - "spectrum estimation from samples"
    - "blinkdb queries with bounded errors and bounded response times on very large data"
    - "a method of moments for mixture models and hidden markov models"
    - "polynomial learning of distribution families"
    - "medians and beyond new aggregation techniques for sensor networks"

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)