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: "on variance reduction in stochastic gradient descent and its asynchronous variants"
  Abstract: We study optimization algorithms based on variance reduction for stochastic gradient descent (SGD). Remarkable recent progress has been made in this direction through development of algorithms like SAG, SVRG, SAGA. These algorithms have been shown to outperform SGD, both theoretically and empirically. However, asynchronous versions of these algorithms---a crucial requirement for modern large-scale applications---have not been studied. We bridge this gap by presenting a unifying framework for many variance reduction techniques. Subsequently, we propose an asynchronous algorithm grounded in our framework, and prove its fast convergence. An important consequence of our general approach is that it yields asynchronous versions of variance reduction algorithms such as SVRG and SAGA as a byproduct. Our method achieves near linear speedup in sparse settings common to machine learning. We demonstrate the empirical performance of our method through a concrete realization of asynchronous SVRG.

Connected through hyperedge HE-1 (induced by source paper "faster on device training using new federated momentum algorithm"):
  with members:
    - "advances and open problems in federated learning"
    - "towards federated learning at scale system design"
    - "federated machine learning concept and applications"
    - "secureboost a lossless federated learning framework"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"
    - "leaf a benchmark for federated settings"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"

Connected through hyperedge HE-2 (induced by source paper "adaptive distributed stochastic gradient descent for minimizing delay in the presence of stragglers"):
  with members:
    - "coded cooperative computation for internet of things"
    - "speeding up distributed machine learning using codes"
    - "anytime stochastic gradient descent a time to hear from all the workers"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "exploitation of stragglers in coded computation"
    - "don t decay the learning rate increase the batch size"
    - "short dot computing large linear transforms distributedly using coded short dot products"

Connected through hyperedge HE-3 (induced by source paper "deam adaptive momentum with discriminative weight for stochastic optimization"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "graph neural networks for small graph and giant network representation learning an overview"
    - "normalized direction preserving adam"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "convergence analysis of proximal gradient with momentum for nonconvex optimization"
    - "improving generalization performance by switching from adam to sgd"
    - "an overview of gradient descent optimization algorithms"

Connected through hyperedge HE-4 (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"
    - "online learning to sample"
    - "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-5 (induced by source paper "dynamic mini batch sgd for elastic distributed training learning in the limbo of resources"):
  with members:
    - "bag of freebies for training object detection neural networks"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "an empirical model of large batch training"
    - "low latency video semantic segmentation"
    - "three factors influencing minima in sgd"
    - "slowfast networks for video recognition"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"

Connected through hyperedge HE-6 (induced by source paper "communication trade offs for synchronized distributed sgd with large step size"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "communication lower bounds for distributed convex optimization partition data on features"
    - "the zipml framework for training models with end to end low precision the cans the cannots and a little bit of deep learning"
    - "federated learning of deep networks using model averaging"

Connected through hyperedge HE-7 (induced by source paper "oversketched newton fast convex optimization for serverless systems"):
  with members:
    - "cloud programming simplified a berkeley view on serverless computing"
    - "harnessing the power of serverless runtimes for large scale optimization"
    - "serverless computing one step forward two steps back"
    - "a distributed second order algorithm you can trust"
    - "speeding up distributed machine learning using codes"
    - "numpywren serverless linear algebra"
    - "oversketch approximate matrix multiplication for the cloud"
    - "giant globally improved approximate newton method for distributed optimization"

Connected through hyperedge HE-8 (induced by source paper "calibrating the adaptive learning rate to improve convergence of adam"):
  with members:
    - "escaping saddle points with adaptive gradient methods"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "an alternative view when does sgd escape local minima"
    - "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"

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)