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 "storyboard optimizing precomputed summaries for aggregation"):
  Abstract: An emerging class of data systems partition their data and precompute approximate summaries (i.e., sketches and samples) for each segment to reduce query costs. They can then aggregate and combine the segment summaries to estimate results without scanning the raw data. However, given limited storage space each summary introduces approximation errors that affect query accuracy. For instance, systems that use existing mergeable summaries cannot reduce query error below the error of an individual precomputed summary. We introduce Storyboard, a query system that optimizes item frequency and quantile summaries for accuracy when aggregating over multiple segments. Compared to conventional mergeable summaries, Storyboard leverages additional memory available for summary construction and aggregation to derive a more precise combined result. This reduces error by up to 25x over interval aggregations and 4.4x over data cube aggregations on industrial datasets compared to standard summarization methods, with provable worst-case error guarantees.

Includes members:
  Member "hillview a trillion cell spreadsheet for big data":
    also connected through other hyperedges:
      (none)
  Member "ddsketch a fast and fully mergeable quantile sketch with relative error guarantees":
    also connected through other hyperedges:
      (none)
  Member "moment based quantile sketches for efficient high cardinality aggregation queries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an experimental evaluation of large scale gbdt systems")
  Member "macrobase prioritizing attention in fast data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automated machine learning state of the art and open challenges")
  Member "optimal quantile approximation in streams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the power of multiple anonymous messages")
  Member "coresets and sketches":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "layered sampling for robust optimization problems")
  Member "structure aware sampling flexible and accurate summarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "data streams with bounded deletions")
  Member "unbiased matrix rounding":
    also connected through other hyperedges:
      (none)

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)