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 "data sketching for faster training of machine learning models"):
  Abstract: Many machine learning problems reduce to the problem of minimizing an expected risk, defined as the sum of a large number of, often convex, component functions. Iterative gradient methods are popular techniques for the above problems. However, they are in general slow to converge, in particular for large data sets. In this work, we develop analysis for selecting a subset (or sketch) of training data points with their corresponding learning rates in order to provide faster convergence to a close neighbordhood of the optimal solution. We show that subsets that minimize the upper-bound on the estimation error of the full gradient, maximize a submodular facility location function. As a result, by greedily maximizing the facility location function we obtain subsets that yield faster convergence to a close neighborhood of the optimum solution. We demonstrate the real-world effectiveness of our algorithm, SIG, confirming our analysis, through an extensive set of experiments on several applications, including logistic regression and training neural networks. We also include a method that provides a deliberate deterministic ordering of the data subset that is quite effective in practice. We observe that our method, while achieving practically the same loss, speeds up gradient methods by up to 10x for convex and 3x for non-convex (deep) functions.

Includes members:
  Member "not all samples are created equal deep learning with importance sampling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "no regret sample selection with noisy labels")
  Member "exploiting the structure stochastic gradient methods using raw clusters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi resolution hashing for fast pairwise summations")
  Member "variance reduced stochastic gradient descent with neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "federated learning of a mixture of global and local models")
  Member "incremental gradient subgradient and proximal methods for convex optimization a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)