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: "variance reduced stochastic gradient descent with neighbors"
  Abstract: Stochastic Gradient Descent (SGD) is a workhorse in machine learning, yet its slow convergence can be a computational bottleneck. Variance reduction techniques such as SAG, SVRG and SAGA have been proposed to overcome this weakness, achieving linear convergence. However, these methods are either based on computations of full gradients at pivot points, or on keeping per data point corrections in memory. Therefore speed-ups relative to SGD may need a minimal number of epochs in order to materialize. This paper investigates algorithms that can exploit neighborhood structure in the training data to share and re-use information about past stochastic gradients across data points, which offers advantages in the transient optimization phase. As a side-product we provide a unified convergence analysis for a family of variance reduction algorithms, which we call memorization algorithms. We provide experimental results supporting our theory.

Connected through hyperedge HE-1 (induced by source paper "federated learning of a mixture of global and local models"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "sgd general analysis and improved rates"
    - "adaptive gradient based meta learning methods"
    - "advances and open problems in federated learning"
    - "tighter theory for local sgd on identical and heterogeneous data"
    - "variational federated multi task learning"
    - "variance reduced local sgd with lower communication complexity"
    - "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"

Connected through hyperedge HE-2 (induced by source paper "adaptivity of stochastic gradient methods for nonconvex optimization"):
  with members:
    - "variance reduction with sparse gradients"
    - "stochastic newton and cubic newton methods with simple local linear quadratic rates"
    - "sgd general analysis and improved rates"
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"
    - "on the convergence of adam and beyond"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "how to make the gradients small stochastically even faster convex and nonconvex sgd"

Connected through hyperedge HE-3 (induced by source paper "reducing the variance in online optimization by transporting past gradients"):
  with members:
    - "anytime tail averaging"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning dexterous in hand manipulation"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "global convergence of policy gradient methods for linearized control problems"
    - "measuring the effects of data parallelism on neural network training"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-4 (induced by source paper "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "direct acceleration of saga using sampled negative momentum"
    - "sdca without duality regularization and individual convexity"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "sdna stochastic dual newton ascent for empirical risk minimization"
    - "finito a faster permutable incremental gradient method for big data problems"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "mini batch primal and dual methods for svms"

Connected through hyperedge HE-5 (induced by source paper "data sketching for faster training of machine learning models"):
  with members:
    - "not all samples are created equal deep learning with importance sampling"
    - "exploiting the structure stochastic gradient methods using raw clusters"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "deep adaptive feature embedding with local sample distributions for person re identification"):
  with members:
    - "scalable person re identification on supervised smoothed manifold"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "re ranking person re identification with k reciprocal encoding"
    - "structured deep hashing with convolutional neural networks for fast person re identification"
    - "hcp a flexible cnn framework for multi label image classification"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "embedding deep metric for person re identication a study against large variations"
    - "learning a discriminative null space for person re identification"

Connected through hyperedge HE-7 (induced by source paper "weighted adagrad with unified momentum"):
  with members:
    - "on the convergence of adam and beyond"
    - "online adaptive methods universality and acceleration"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "convergence guarantees for rmsprop and adam in non convex optimization and an empirical comparison to nesterov acceleration"
    - "variants of rmsprop and adagrad with logarithmic regret bounds"
    - "adabatch efficient gradient aggregation rules for sequential and parallel stochastic gradient methods"
    - "adam a method for stochastic optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-8 (induced by source paper "fast variance reduction method with stochastic batch size"):
  with members:
    - "big batch sgd automated inference using adaptive batch sizes"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "stop wasting my gradients practical svrg"
    - "adam a method for stochastic optimization"
    - "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)