human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "moment based quantile sketches for efficient high cardinality aggregation queries"):
  Abstract: Interactive analytics increasingly involves querying for quantiles over specific sub-populations and time windows of high cardinality datasets. Data processing engines such as Druid and Spark use mergeable summaries to estimate quantiles on these large datasets, but summary merge times are a bottleneck during high-cardinality aggregation. We show how a compact and efficiently mergeable quantile sketch can support aggregation workloads. This data structure, which we refer to as the moments sketch, operates with a small memory footprint (200 bytes) and computationally efficient (50ns) merges by tracking only a set of summary statistics, notably the sample moments. We demonstrate how we can efficiently and practically estimate quantiles using the method of moments and the maximum entropy principle, and show how the use of a cascade further improves query time for threshold predicates. Empirical evaluation on real-world datasets shows that the moments sketch can achieve less than 1 percent error with 40 times less merge overhead than comparable summaries, improving end query time in the MacroBase engine by up to 7 times and the Druid engine by up to 60 times.

Includes members:
  Member "an efficient bandit algorithm for realtime multivariate optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "decoupling learning rates using empirical bayes priors")
  Member "macrobase prioritizing attention in fast data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "storyboard optimizing precomputed summaries for aggregation")
  Member "spectrum estimation from samples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension")
  Member "an experimental study of distributed quantile estimation":
    also connected through other hyperedges:
      (none)
  Member "blinkdb queries with bounded errors and bounded response times on very large data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ml aqp query driven approximate query processing based on machine learning")
  Member "a method of moments for mixture models and hidden markov models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provable meta learning of linear representations")
  Member "polynomial learning of distribution families":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "private hypothesis selection")
  Member "medians and beyond new aggregation techniques for sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "streaming algorithms for bin packing and vector scheduling")

Question: Please tell me which class the source paper 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)