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 "efficient data driven geologic feature characterization from pre stack seismic measurements using randomized machine learning algorithm"):
  Abstract: Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency, and subjective human factors. We developed a novel data-driven geological feature detection approach based on pre-stack seismic measurements. Our detection method employs an efficient and accurate machine-learning detection approach to extract useful subsurface geologic features automatically. Specifically, our method is based on kernel ridge regression model. The conventional kernel ridge regression can be computationally prohibited because of the large volume of seismic measurements. We employ a data reduction technique in combination with the conventional kernel ridge regression method to improve the computational efficiency and reduce memory usage. In particular, we utilize a randomized numerical linear algebra technique, named Nystr\"om method, to effectively reduce the dimensionality of the feature space without compromising the information content required for accurate detection. We provide thorough computational cost analysis to show efficiency of our new geological feature detection methods. We further validate the performance of our new subsurface geologic feature detection method using synthetic surface seismic data for 2D acoustic and elastic velocity models. Our numerical examples demonstrate that our new detection method significantly improves the computational efficiency while maintaining comparable accuracy. Interestingly, we show that our method yields a speed-up ratio on the order of $\sim10^2$ to $\sim 10^3$ in a multi-core computational environment.

Includes members:
  Member "scalable kernel k means clustering with nystrom approximation relative error bounds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "diversity sampling is an implicit regularization for kernel methods")
  Member "large scale kernel learning using block coordinate descent":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beyond least squares fast rates for regularized empirical risk minimization through self concordance")
  Member "hierarchically compositional kernels for scalable nonparametric learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faster kernel ridge regression using sketching and preconditioning")
  Member "faster kernel ridge regression using sketching and preconditioning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "serverless straggler mitigation using local error correcting codes")
  Member "towards more efficient spsd matrix approximation and cur matrix decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast generalized matrix regression with applications in machine learning")
  Member "a practical guide to randomized matrix computations with matlab implementations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weighted gradient coding with leverage score sampling")
  Member "revisiting the nystrom method for improved large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "sharp analysis of low rank kernel matrix approximations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "diversity sampling is an implicit regularization for kernel methods")

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.LG(Machine Learning)