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 "effective sketching methods for value function approximation"):
  Abstract: High-dimensional representations, such as radial basis function networks or tile coding, are common choices for policy evaluation in reinforcement learning. Learning with such high-dimensional representations, however, can be expensive, particularly for matrix methods, such as least-squares temporal difference learning or quasi-Newton methods that approximate matrix step-sizes. In this work, we explore the utility of sketching for these two classes of algorithms. We highlight issues with sketching the high-dimensional features directly, which can incur significant bias. As a remedy, we demonstrate how to use sketching more sparingly, with only a left-sided sketch, that can still enable significant computational gains and the use of these matrix-based learning algorithms that are less sensitive to parameters. We empirically investigate these algorithms, in four domains with a variety of representations. Our aim is to provide insights into effective use of sketching in practice.

Includes members:
  Member "efficient second order online learning by sketching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive online learning with varying norms")
  Member "accelerated gradient temporal difference learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "kalman meets bellman improving policy evaluation through value tracking")
  Member "unifying task specification in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "options of interest temporal abstraction with interest functions")
  Member "communication optimal distributed principal component analysis in the column partition model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "greedy column subset selection new bounds and distributed algorithms")
  Member "a practical guide to randomized matrix computations with matlab implementations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weighted gradient coding with leverage score sampling")
  Member "optimal principal component analysis in distributed and streaming models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "on convergence of emphatic temporal difference learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "provably convergent two timescale off policy actor critic with function approximation")
  Member "sketching as a tool for numerical linear algebra":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximating spectral clustering via sampling a review")

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)