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: "rapid sampling for visualizations with ordering guarantees"
  Abstract: Visualizations are frequently used as a means to understand trends and gather insights from datasets, but often take a long time to generate. In this paper, we focus on the problem of rapidly generating approximate visualizations while preserving crucial visual proper- ties of interest to analysts. Our primary focus will be on sampling algorithms that preserve the visual property of ordering; our techniques will also apply to some other visual properties. For instance, our algorithms can be used to generate an approximate visualization of a bar chart very rapidly, where the comparisons between any two bars are correct. We formally show that our sampling algorithms are generally applicable and provably optimal in theory, in that they do not take more samples than necessary to generate the visualizations with ordering guarantees. They also work well in practice, correctly ordering output groups while taking orders of magnitude fewer samples and much less time than conventional sampling schemes.

Connected through hyperedge HE-1 (induced by source paper "towards scalable dataframe systems"):
  with members:
    - "verdictdb universalizing approximate query processing"
    - "helix holistic optimization for accelerating iterative machine learning"
    - "ray a distributed framework for emerging ai applications"
    - "adaptive sampling for rapidly matching histograms"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "towards a holistic integration of spreadsheets with databases a scalable storage engine for presentational data management"
    - "lara a key value algebra underlying arrays and relations"
    - "visualization aware sampling for very large databases"

Connected through hyperedge HE-2 (induced by source paper "hillview a trillion cell spreadsheet for big data"):
  with members:
    - "big data visualization tools"
    - "foresight recommending visual insights"
    - "progressive analytics a computation paradigm for exploratory data analysis"
    - "visualization aware sampling for very large databases"
    - "a hierarchical aggregation framework for efficient multilevel visual exploration and analysis"
    - "a tutorial on principal component analysis"
    - "bottom k and priority sampling set similarity and subset sums with minimal independence"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-3 (induced by source paper "rsatree distribution aware data representation of large scale tabular datasets for flexible visual query"):
  with members:
    - "the case for learned index structures"
    - "progressive analytics a computation paradigm for exploratory data analysis"
    - "visualization aware sampling for very large databases"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-4 (induced by source paper "probery a probability based incomplete query optimization for big data"):
  with members:
    - "a comparative analysis of state of the art sql on hadoop systems for interactive analytics"
    - "visualization aware sampling for very large databases"
    - "interactive data exploration with smart drill down"

Connected through hyperedge HE-5 (induced by source paper "progressive data science potential and challenges"):
  with members:
    - "a survey on deep learning toolkits and libraries for intelligent user interfaces"
    - "helix holistic optimization for accelerating iterative machine learning"
    - "progressive neural networks"
    - "generative adversarial active learning"
    - "enabling robots to communicate their objectives"
    - "data polygamy the many many relationships among urban spatio temporal data sets"
    - "progressive analytics a computation paradigm for exploratory data analysis"
    - "revisiting reuse in main memory database systems"

Connected through hyperedge HE-6 (induced by source paper "provenance for interactive visualizations"):
  with members:
    - "seq2seq vis a visual debugging tool for sequence to sequence models"
    - "smoke fine grained lineage at interactive speed"
    - "algorithms for provisioning queries and analytics"
    - "timemachine timeline generation for knowledge base entities"

Connected through hyperedge HE-7 (induced by source paper "vizrec a framework for secure data exploration via visual representation"):
  with members:
    - "bias in olap queries detection explanation and removal"
    - "data polygamy the many many relationships among urban spatio temporal data sets"
    - "controlling false discoveries during interactive data exploration"
    - "the vc dimension of queries and selectivity estimation through sampling"

Connected through hyperedge HE-8 (induced by source paper "miss finding optimal sample sizes for approximate analytics"):
  with members:
    - "simulation optimization a review of algorithms and applications"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

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)