human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "online learning to sample"
  Abstract: Stochastic Gradient Descent (SGD) is one of the most widely used techniques for online optimization in machine learning. In this work, we accelerate SGD by adaptively learning how to sample the most useful training examples at each time step. First, we show that SGD can be used to learn the best possible sampling distribution of an importance sampling estimator. Second, we show that the sampling distribution of an SGD algorithm can be estimated online by incrementally minimizing the variance of the gradient. The resulting algorithm - called Adaptive Weighted SGD (AW-SGD) - maintains a set of parameters to optimize, as well as a set of parameters to sample learning examples. We show that AWSGD yields faster convergence in three different applications: (i) image classification with deep features, where the sampling of images depends on their labels, (ii) matrix factorization, where rows and columns are not sampled uniformly, and (iii) reinforcement learning, where the optimized and exploration policies are estimated at the same time, where our approach corresponds to an off-policy gradient algorithm.

Connected through hyperedge HE-1 (induced by source paper "adaptive sampling distributed stochastic variance reduced gradient for heterogeneous distributed datasets"):
  with members:
    - "not all samples are created equal deep learning with importance sampling"
    - "safe adaptive importance sampling"
    - "biased importance sampling for deep neural network training"
    - "federated optimization distributed machine learning for on device intelligence"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-2 (induced by source paper "sgd with hardness weighted sampling for distributionally robust deep learning"):
  with members:
    - "an improved analysis of training over parameterized deep neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "on the convergence rate of training recurrent neural networks"
    - "niftynet a deep learning platform for medical imaging"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "a convergence theory for deep learning via over parameterization"
    - "training region based object detectors with online hard example mining"
    - "wide residual networks"

Connected through hyperedge HE-3 (induced by source paper "online variance reduction with mixtures"):
  with members:
    - "introduction to online convex optimization"
    - "not all samples are created equal deep learning with importance sampling"
    - "coordinate descent with bandit sampling"
    - "faster coordinate descent via adaptive importance sampling"
    - "safe adaptive importance sampling"
    - "prioritized experience replay"
    - "online batch selection for faster training of neural networks"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-4 (induced by source paper "big batch sgd automated inference using adaptive batch sizes"):
  with members:
    - "importance sampling for minibatches"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "probabilistic line searches for stochastic optimization"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "finito a faster permutable incremental gradient method for big data problems"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "minimizing the maximal loss how and why"):
  with members:
    - "accelerating stochastic gradient descent via online learning to sample"
    - "sublinear optimization for machine learning"

Connected through hyperedge HE-6 (induced by source paper "scaling up distributed stochastic gradient descent using variance reduction"):
  with members:
    - "importance sampling for minibatches"
    - "without replacement sampling for stochastic gradient methods convergence results and application to distributed optimization"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "stop wasting my gradients practical svrg"
    - "finito a faster permutable incremental gradient method for big data problems"
    - "deep learning with elastic averaging sgd"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Question: Please tell me which class the center node 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)