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 "infiniviz interactive visual exploration using progressive bin refinement"):
  Abstract: Interactive visualizations can accelerate the data analysis loop through near-instantaneous feedback. To achieve interactivity, techniques such as data cubes and sampling are typically employed. While data cubes can speedup querying for moderate-sized datasets, they are ineffective at doing so at a larger scales due to the size of the materialized data cubes. On the other hand, while sampling can help scale to large datasets, it adds sampling error and the associated issues into the process. #R##N#While increasing accuracy by looking at more data may sometimes be valuable, providing result minutiae might not be necessary if they do not impart additional significant information. Indeed, such details not only incur a higher \emph{computational} cost, but also tax the \emph{cognitive} load of the analyst with worthless trivia. To reduce both the computational and cognitive expenses, we introduce \emph{InfiniViz}. Through a novel result refinement-based querying paradigm, \emph{InfiniViz} provides error-free results for large datasets by increasing bin resolutions progressively over time. Through real and simulated workloads over real and benchmark datasets, we evaluate and demonstrate \emph{InfiniViz}'s utility at reducing both cognitive and computational costs, while minimizing information loss.

Includes members:
  Member "effortless data exploration with zenvisage an expressive and interactive visual analytics system":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automated machine learning state of the art and open challenges")
  Member "progressive analytics a computation paradigm for exploratory data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hillview a trillion cell spreadsheet for big data")
  Member "visualization aware sampling for very large databases":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards scalable dataframe systems")
  Member "a sampling algebra for aggregate estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "verdictdb universalizing approximate query processing")

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)