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 "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.

Includes members:
  Member "rapid sampling for visualizations with ordering guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards scalable dataframe systems")
  Member "blinkdb queries with bounded errors and bounded response times on very large data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ml aqp query driven approximate query processing based on machine learning")

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)