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: "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.

Connected through hyperedge HE-1 (induced by source paper "storyboard optimizing precomputed summaries for aggregation"):
  with members:
    - "hillview a trillion cell spreadsheet for big data"
    - "ddsketch a fast and fully mergeable quantile sketch with relative error guarantees"
    - "macrobase prioritizing attention in fast data"
    - "optimal quantile approximation in streams"
    - "coresets and sketches"
    - "structure aware sampling flexible and accurate summarization"
    - "unbiased matrix rounding"

Connected through hyperedge HE-2 (induced by source paper "an experimental evaluation of large scale gbdt systems"):
  with members:
    - "deep forest towards an alternative to deep neural networks"
    - "optimal quantile approximation in streams"
    - "xgboost a scalable tree boosting system"
    - "dimmwitted a study of main memory statistical analytics"
    - "robust logitboost and adaptive base class abc logitboost"

Connected through hyperedge HE-3 (induced by source paper "approximate quantiles for datacenter telemetry monitoring"):
  with members:
    - "streaming quantiles algorithms with small space and update time"

Connected through hyperedge HE-4 (induced by source paper "ddsketch a fast and fully mergeable quantile sketch with relative error guarantees"):
  with members:
    - "optimal quantile approximation in streams"

Connected through hyperedge HE-5 (induced by source paper "chasing similarity distribution aware aggregation scheduling extended version"):
  with members:
    - "parameter hub a rack scale parameter server for distributed deep neural network training"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "bayesian locality sensitive hashing for fast similarity search"
    - "reductions between expansion problems"

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)