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: "revisiting asynchronous linear solvers provable convergence rate through randomization"
  Abstract: Asynchronous methods for solving systems of linear equations have been researched since Chazan and Miranker's pioneering 1969 paper on chaotic relaxation. The underlying idea of asynchronous methods is to avoid processor idle time by allowing the processors to continue to make progress even if not all progress made by other processors has been communicated to them. #R##N#Historically, the applicability of asynchronous methods for solving linear equations was limited to certain restricted classes of matrices, such as diagonally dominant matrices. Furthermore, analysis of these methods focused on proving convergence in the limit. Comparison of the asynchronous convergence rate with its synchronous counterpart and its scaling with the number of processors were seldom studied, and are still not well understood. #R##N#In this paper, we propose a randomized shared-memory asynchronous method for general symmetric positive definite matrices. We rigorously analyze the convergence rate and prove that it is linear, and is close to that of the method's synchronous counterpart if the processor count is not excessive relative to the size and sparsity of the matrix. We also present an algorithm for unsymmetric systems and overdetermined least-squares. Our work presents a significant improvement in the applicability of asynchronous linear solvers as well as in their convergence analysis, and suggests randomization as a key paradigm to serve as a foundation for asynchronous methods.

Connected through hyperedge HE-1 (induced by source paper "distributed machine learning on mobile devices a survey"):
  with members:
    - "towards federated learning at scale system design"
    - "robust and communication efficient federated learning from non iid data"
    - "on the convergence of adam and beyond"
    - "federated machine learning concept and applications"
    - "secureboost a lossless federated learning framework"
    - "federated learning of out of vocabulary words"
    - "applied federated learning improving google keyboard query suggestions"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-2 (induced by source paper "asynchronous stochastic proximal methods for nonconvex nonsmooth optimization"):
  with members:
    - "ray a distributed framework for emerging ai applications"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "deep learning with elastic averaging sgd"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-3 (induced by source paper "a model parallel proximal stochastic gradient algorithm for partially asynchronous systems"):
  with members:
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "deep learning with elastic averaging sgd"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-4 (induced by source paper "an asynchronous parallel approach to sparse recovery"):
  with members:
    - "asynchronous stochastic gradient descent with variance reduction for non convex optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "asynchronous stochastic gradient descent with delay compensation"):
  with members:
    - "revisiting distributed synchronous sgd"
    - "convolutional sequence to sequence learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "asynchronous stochastic block coordinate descent with variance reduction"):
  with members:
    - "asynchronous stochastic proximal optimization algorithms with variance reduction"
    - "distributed asynchronous dual free stochastic dual coordinate ascent"
    - "sdca without duality regularization and individual convexity"
    - "decoupled asynchronous proximal stochastic gradient descent with variance reduction"
    - "asynchronous stochastic gradient descent with variance reduction for non convex optimization"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "parallel coordinate descent for l1 regularized loss minimization"

Connected through hyperedge HE-7 (induced by source paper "passcode parallel asynchronous stochastic dual co ordinate descent"):
  with members:
    - "communication efficient distributed dual coordinate ascent"
    - "parallel coordinate descent for l1 regularized loss minimization"

Connected through hyperedge HE-8 (induced by source paper "sublinear column wise actions of the matrix exponential on social networks"):
  with members:
    - "heat kernel based community detection"
    - "fast matrix computations for pairwise and columnwise commute times and katz scores"
    - "defining and evaluating network communities based on ground truth"
    - "approximating the exponential the lanczos method and an tilde o m time spectral algorithm for balanced separator"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social 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.DC(Distributed, Parallel, and Cluster Computing)