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 elastic net regularization for sparse linear models"):
  Abstract: We extend previous work on efficiently training linear model s by applying stochastic updates to non-zero features only, lazily bring ing weights current as needed. To date, only the closed form updates for the l1, l1, and the rarely used l2 norm have been described. We extend this work by showing the proper closed form updates for the popular l 2 and elastic net regularized models. We show a dynamic programming algorithm to calculate the proper elastic net update with only one constant-time subproblem computation per update. Our algorithm handles both fixed and decreasing learning rates and we derive th e result for both stochastic gradient descent (SGD) and forward backward splitting (FoBoS) . We empirically validate the algorithm, showing that on a bag-of-words dataset with 260, 941 features and 88 nonzero features on average per example, our method trains a logistic regression classifier with elastic net reg ularization 612 times faster than an otherwise identical implementation with dense updates.

Includes members:
  Member "adadelta an adaptive learning rate method":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "sparse online learning via truncated gradient":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "full deep neural network training on a pruned weight budget")

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)