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: "importance sampling for minibatches"
  Abstract: Minibatching is a very well studied and highly popular technique in supervised learning, used by practitioners due to its ability to accelerate training through better utilization of parallel processing power and reduction of stochastic variance. Another popular technique is importance sampling -- a strategy for preferential sampling of more important examples also capable of accelerating the training process. However, despite considerable effort by the community in these areas, and due to the inherent technical difficulty of the problem, there is no existing work combining the power of importance sampling with the strength of minibatching. In this paper we propose the first {\em importance sampling for minibatches} and give simple and rigorous complexity analysis of its performance. We illustrate on synthetic problems that for training data of certain properties, our sampling can lead to several orders of magnitude improvement in training time. We then test the new sampling on several popular datasets, and show that the improvement can reach an order of magnitude.

Connected through hyperedge HE-1 (induced by source paper "stochastic trust region inexact newton method for large scale machine learning"):
  with members:
    - "saags biased stochastic variance reduction methods for large scale learning"
    - "libs2ml a library for scalable second order machine learning algorithms"
    - "vr sgd a simple stochastic variance reduction method for machine learning"
    - "saags biased stochastic variance reduction methods"
    - "faster learning by reduction of data access time"
    - "a variance reduced stochastic newton method"
    - "res regularized stochastic bfgs algorithm"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-2 (induced by source paper "improving sampling accuracy of stochastic gradient mcmc methods via non uniform subsampling of gradients"):
  with members:
    - "the case for bayesian deep learning"
    - "bayesian posterior sampling via stochastic gradient fisher scoring"

Connected through hyperedge HE-3 (induced by source paper "accelerating minibatch stochastic gradient descent using typicality sampling"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "learning what data to learn"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "big batch sgd automated inference using adaptive batch sizes"
    - "wavenet a generative model for raw audio"
    - "online batch selection for faster training of neural networks"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "99 of distributed optimization is a waste of time the issue and how to fix it"):
  with members:
    - "distributed learning with compressed gradient differences"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "signsgd compressed optimisation for non convex problems"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "gradient sparsification for communication efficient distributed optimization"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "adding vs averaging in distributed primal dual optimization"

Connected through hyperedge HE-5 (induced by source paper "sgd general analysis and improved rates"):
  with members:
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "stochastic gradient push for distributed deep learning"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "sdca without duality regularization and individual convexity"
    - "train faster generalize better stability of stochastic gradient descent"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "accelerating mini batch sarah by step size rules"):
  with members:
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "online learning rate adaptation with hypergradient descent"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "probabilistic line searches for stochastic optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "better mini batch algorithms via accelerated gradient methods"

Connected through hyperedge HE-7 (induced by source paper "saga with arbitrary sampling"):
  with members:
    - "sdca without duality regularization and individual convexity"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-8 (induced by source paper "perfect match a simple method for learning representations for counterfactual inference with neural networks"):
  with members:
    - "not to cry wolf distantly supervised multitask learning in critical care"
    - "granger causal attentive mixtures of experts learning important features with neural networks"
    - "confounding robust policy improvement"
    - "deep counterfactual networks with propensity dropout"
    - "bayesian inference of individualized treatment effects using multi task gaussian processes"
    - "domain adaptation learning bounds and algorithms"

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)