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: "fast large scale optimization by unifying stochastic gradient and quasi newton methods"
  Abstract: We present an algorithm for minimizing a sum of functions that combines the computational efficiency of stochastic gradient descent (SGD) with the second order curvature information leveraged by quasi-Newton methods. We unify these disparate approaches by maintaining an independent Hessian approximation for each contributing function in the sum. We maintain computational tractability and limit memory requirements even for high dimensional optimization problems by storing and manipulating these quadratic approximations in a shared, time evolving, low dimensional subspace. Each update step requires only a single contributing function or minibatch evaluation (as in SGD), and each step is scaled using an approximate inverse Hessian and little to no adjustment of hyperparameters is required (as is typical for quasi-Newton methods). This algorithm contrasts with earlier stochastic second order techniques that treat the Hessian of each contributing function as a noisy approximation to the full Hessian, rather than as a target for direct estimation. We experimentally demonstrate improved convergence on seven diverse optimization problems. The algorithm is released as open source Python and MATLAB packages.

Connected through hyperedge HE-1 (induced by source paper "analysis of dawnbench a time to accuracy machine learning performance benchmark"):
  with members:
    - "beyond data and model parallelism for deep neural networks"
    - "nvidia tensor core programmability performance precision"
    - "an empirical model of large batch training"
    - "regularized evolution for image classifier architecture search"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "exploring the limits of weakly supervised pretraining"
    - "revisiting small batch training for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "neumann optimizer a practical optimization algorithm for deep neural networks"):
  with members:
    - "imagenet training in minutes"
    - "eigenvalues of the hessian in deep learning singularity and beyond"
    - "scaling sgd batch size to 32k for imagenet training"
    - "tacotron towards end to end speech synthesis"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-3 (induced by source paper "efficient second order online learning by sketching"):
  with members:
    - "solving ridge regression using sketched preconditioned svrg"
    - "efficient frequent directions algorithm for sparse matrices"
    - "the fast convergence of incremental pca"
    - "scale free algorithms for online linear optimization"
    - "frequent directions simple and deterministic matrix sketching"
    - "faster sgd using sketched conditioning"
    - "sketching as a tool for numerical linear algebra"
    - "a linearly convergent conditional gradient algorithm with applications to online and stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "empirical study of proxtone and proxtone for fast learning of large scale sparse models"):
  with members:
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-5 (induced by source paper "adaqn an adaptive quasi newton algorithm for training rnns"):
  with members:
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "visualizing and understanding recurrent networks"
    - "train faster generalize better stability of stochastic gradient descent"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "adam a method for stochastic optimization"
    - "res regularized stochastic bfgs algorithm"
    - "recurrent neural network regularization"
    - "new insights and perspectives on the natural gradient method"

Connected through hyperedge HE-6 (induced by source paper "deep unsupervised learning using nonequilibrium thermodynamics"):
  with members:
    - "nice non linear independent components estimation"
    - "reweighted wake sleep"
    - "accurate and conservative estimates of mrf log likelihood using reverse annealing"
    - "loopy belief propagation for approximate inference an empirical study"
    - "deep generative stochastic networks trainable by backprop"
    - "deep autoregressive networks"
    - "better mixing via deep representations"
    - "fast inference in sparse coding algorithms with applications to object recognition"

Connected through hyperedge HE-7 (induced by source paper "sdna stochastic dual newton ascent for empirical risk minimization"):
  with members:
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "semi stochastic coordinate descent"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "smooth minimization of nonsmooth functions with parallel coordinate descent methods"
    - "mini batch primal and dual methods for svms"

Connected through hyperedge HE-8 (induced by source paper "tensor machines for learning target specific polynomial features"):
  with members:
    - "a la carte learning fast kernels"
    - "scalable nonlinear learning with adaptive polynomial expansions"
    - "sparse polynomial learning and graph sketching"
    - "an algorithm for training polynomial networks"

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)