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: "memory and communication efficient distributed stochastic optimization with minibatch prox"
  Abstract: We present and analyze an approach for distributed stochastic optimization which is statistically optimal and achieves near-linear speedups (up to logarithmic factors). Our approach allows a communication-memory tradeoff, with either logarithmic communication but linear memory, or polynomial communication and a corresponding polynomial reduction in required memory. This communication-memory tradeoff is achieved through minibatch-prox iterations (minibatch passive-aggressive updates), where a subproblem on a minibatch is solved at each iteration. We provide a novel analysis for such a minibatch-prox procedure which achieves the statistical optimal rate regardless of minibatch size and smoothness, thus significantly improving on prior work.

Connected through hyperedge HE-1 (induced by source paper "is local sgd better than minibatch sgd"):
  with members:
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "unified optimal analysis of the stochastic gradient method"
    - "local sgd with periodic averaging tighter analysis and adaptive synchronization"
    - "on the randomized complexity of minimizing a convex quadratic function"
    - "don t use large mini batches use local sgd"
    - "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-2 (induced by source paper "a distributed hierarchical sgd algorithm with sparse global reduction"):
  with members:
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "don t use large mini batches use local sgd"
    - "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "revisiting distributed synchronous sgd"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "sgdr stochastic gradient descent with restarts"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "convergence of distributed stochastic variance reduced methods without sampling extra data"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "signsgd compressed optimisation for non convex problems"
    - "giant globally improved approximate newton method for distributed optimization"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "federated optimization distributed optimization beyond the datacenter"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-4 (induced by source paper "stochastic approximation of smooth and strongly convex functions beyond the o 1 t convergence rate"):
  with members:
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"
    - "from exp concavity to variance control high probability o 1 n rates and high probability online to batch conversion"
    - "generalization of erm in stochastic convex optimization the dimension strikes back"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "passive learning with target risk"
    - "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-5 (induced by source paper "don t use large mini batches use local sgd"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "advances and open problems in federated learning"
    - "communication trade offs for synchronized distributed sgd with large step size"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "on the ineffectiveness of variance reduced optimization for deep learning"
    - "federated learning strategies for improving communication efficiency"
    - "an empirical model of large batch training"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"

Connected through hyperedge HE-6 (induced by source paper "asvrg accelerated proximal svrg"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "vr sgd a simple stochastic variance reduction method for machine learning"
    - "accelerated variance reduced stochastic admm"
    - "fast stochastic variance reduced gradient method with momentum acceleration for machine learning"
    - "variance reduced and projection free stochastic optimization"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "an overview of gradient descent optimization algorithms"

Connected through hyperedge HE-7 (induced by source paper "cola decentralized linear learning"):
  with members:
    - "a distributed second order algorithm you can trust"
    - "federated learning strategies for improving communication efficiency"
    - "communication compression for decentralized training"
    - "sparsified sgd with memory"
    - "d 2 decentralized training over decentralized data"
    - "federated multi task learning"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "primal dual rates and certificates"

Connected through hyperedge HE-8 (induced by source paper "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization"):
  with members:
    - "revisiting distributed synchronous sgd"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "sgdr stochastic gradient descent with restarts"
    - "very deep convolutional networks for large scale image recognition"
    - "network in network"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "optimal distributed online prediction using mini batches"

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)