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: "visualization aware sampling for very large databases"
  Abstract: Interactive visualizations are crucial in ad hoc data exploration and analysis. However, with the growing number of massive datasets, generating visualizations in interactive timescales is increasingly challenging. One approach for improving the speed of the visualization tool is via data reduction in order to reduce the computational overhead, but at a potential cost in visualization accuracy. Common data reduction techniques, such as uniform and stratified sampling, do not exploit the fact that the sampled tuples will be transformed into a visualization for human consumption. #R##N#We propose a visualization-aware sampling (VAS) that guarantees high quality visualizations with a small subset of the entire dataset. We validate our method when applied to scatter and map plots for three common visualization goals: regression, density estimation, and clustering. The key to our sampling method's success is in choosing tuples which minimize a visualization-inspired loss function. Our user study confirms that optimizing this loss function correlates strongly with user success in using the resulting visualizations. We also show the NP-hardness of our optimization problem and propose an efficient approximation algorithm. Our experiments show that, compared to previous methods, (i) using the same sample size, VAS improves user's success by up to 35% in various visualization tasks, and (ii) VAS can achieve a required visualization quality up to 400 times faster.

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"
    - "rapid sampling for visualizations with ordering guarantees"

Connected through hyperedge HE-2 (induced by source paper "blinkml efficient maximum likelihood estimation with probabilistic guarantees"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "verdictdb universalizing approximate query processing"
    - "measuring the effects of data parallelism on neural network training"
    - "hemingway modeling distributed optimization algorithms"
    - "unbiased estimates for linear regression via volume sampling"
    - "database learning toward a database that becomes smarter every time"
    - "noscope optimizing neural network queries over video at scale"
    - "clipper a low latency online prediction serving system"

Connected through hyperedge HE-3 (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"
    - "a hierarchical aggregation framework for efficient multilevel visual exploration and analysis"
    - "rapid sampling for visualizations with ordering guarantees"
    - "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-4 (induced by source paper "joins on samples a theoretical guide for practitioners"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "verdictdb universalizing approximate query processing"
    - "quicksel quick selectivity learning with mixture models"
    - "size bounds and query plans for relational joins"
    - "sampling based query re optimization"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-5 (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"
    - "rapid sampling for visualizations with ordering guarantees"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-6 (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"
    - "rapid sampling for visualizations with ordering guarantees"
    - "interactive data exploration with smart drill down"

Connected through hyperedge HE-7 (induced by source paper "big data visualization tools"):
  with members:
    - "graphvizdb a scalable platform for interactive large graph visualization"
    - "dinodb an interactive speed query engine for ad hoc queries on temporary data"
    - "exploration and visualization in the web of big linked data a survey of the state of the art"
    - "a hierarchical aggregation framework for efficient multilevel visual exploration and analysis"
    - "large graph analysis in the gmine system"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-8 (induced by source paper "verdictdb universalizing approximate query processing"):
  with members:
    - "database learning toward a database that becomes smarter every time"
    - "approximate lifted inference with probabilistic databases"
    - "a sampling algebra for aggregate estimation"
    - "blinkdb queries with bounded errors and bounded response times on very large data"
    - "the big data bootstrap"
    - "early accurate results for advanced analytics on mapreduce"

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)