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 "speculative approximations for terascale analytics"):
  Abstract: Author(s): Qin, Chengjie; Rusu, Florin | Abstract: Model calibration is a major challenge faced by the plethora of statistical analytics packages that are increasingly used in Big Data applications. Identifying the optimal model parameters is a time-consuming process that has to be executed from scratch for every dataset/model combination even by experienced data scientists. We argue that the incapacity to evaluate multiple parameter configurations simultaneously and the lack of support to quickly identify sub-optimal configurations are the principal causes. In this paper, we develop two database-inspired techniques for efficient model calibration. Speculative parameter testing applies advanced parallel multi-query processing methods to evaluate several configurations concurrently. The number of configurations is determined adaptively at runtime, while the configurations themselves are extracted from a distribution that is continuously learned following a Bayesian process. Online aggregation is applied to identify sub-optimal configurations early in the processing by incrementally sampling the training dataset and estimating the objective function corresponding to each configuration. We design concurrent online aggregation estimators and define halting conditions to accurately and timely stop the execution. We apply the proposed techniques to distributed gradient descent optimization -- batch and incremental -- for support vector machines and logistic regression models. We implement the resulting solutions in GLADE PF-OLA -- a state-of-the-art Big Data analytics system -- and evaluate their performance over terascale-size synthetic and real datasets. The results confirm that as many as 32 configurations can be evaluated concurrently almost as fast as one, while sub-optimal configurations are detected accurately in as little as a $1/20^{\text{th}}$ fraction of the time.

Includes members:
  Member "incremental gradient subgradient and proximal methods for convex optimization a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent")
  Member "dimmwitted a study of main memory statistical analytics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed learning of deep neural networks using independent subnet training")
  Member "mli an api for distributed machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tf replicator distributed machine learning for researchers")
  Member "the madlib analytics library or mad skills the sql":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi layer optimizations for end to end data analytics")
  Member "towards a unified architecture for in rdbms analytics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi layer optimizations for end to end data analytics")
  Member "early accurate results for advanced analytics on mapreduce":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hillview a trillion cell spreadsheet for big data")
  Member "a reliable effective terascale linear learning system":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stannis low power acceleration of deep neural network training using computational storage")
  Member "optimal distributed online prediction using mini batches":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "communication optimization strategies for distributed deep learning a survey")

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)