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: "without replacement sampling for stochastic gradient methods convergence results and application to distributed optimization"
  Abstract: Stochastic gradient methods for machine learning and optimization problems are usually analyzed assuming data points are sampled \emph{with} replacement. In practice, however, sampling \emph{without} replacement is very common, easier to implement in many cases, and often performs better. In this paper, we provide competitive convergence guarantees for without-replacement sampling, under various scenarios, for three types of algorithms: Any algorithm with online regret guarantees, stochastic gradient descent, and SVRG. A useful application of our SVRG analysis is a nearly-optimal algorithm for regularized least squares in a distributed setting, in terms of both communication complexity and runtime complexity, when the data is randomly partitioned and the condition number can be as large as the data size per machine (up to logarithmic factors). Our proof techniques combine ideas from stochastic optimization, adversarial online learning, and transductive learning theory, and can potentially be applied to other stochastic optimization and learning problems.

Connected through hyperedge HE-1 (induced by source paper "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"):
  with members:
    - "hypothesis set stability and generalization"
    - "generalization bounds for uniformly stable algorithms"
    - "privacy preserving prediction"
    - "tight analyses for non smooth stochastic gradient descent"
    - "statistical optimality of stochastic gradient descent on hard learning problems through multiple passes"
    - "data dependent stability of stochastic gradient descent"
    - "subgaussian tail bounds via stability arguments"
    - "concentration bounds for high sensitivity functions through differential privacy"

Connected through hyperedge HE-2 (induced by source paper "stochastic learning under random reshuffling with constant step sizes"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "variance reduced stochastic learning under random reshuffling"
    - "efficient distributed sgd with variance reduction"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-3 (induced by source paper "near optimal coded data shuffling for distributed learning"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "speeding up distributed machine learning using codes"
    - "on the fundamental limits of coded data shuffling for distributed learning systems"
    - "a pliable index coding approach to data shuffling"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "low subpacketization schemes for coded caching"
    - "latency analysis of coded computation schemes over wireless networks"
    - "communication vs distributed computation an alternative trade off curve"

Connected through hyperedge HE-4 (induced by source paper "decentralized differentially private without replacement stochastic gradient descent"):
  with members:
    - "differentially private empirical risk minimization revisited faster and more general"
    - "privacy amplification by iteration"
    - "efficient private erm for smooth objectives"
    - "collecting and analyzing data from smart device users with local differential privacy"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "train faster generalize better stability of stochastic gradient descent"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "differentially private online learning"

Connected through hyperedge HE-5 (induced by source paper "proving expected sensitivity of probabilistic programs"):
  with members:
    - "a semantic account of metric preservation"
    - "developing bug free machine learning systems with formal mathematics"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "liftings for differential privacy"
    - "coupling proofs are probabilistic product programs"
    - "probabilistic couplings for probabilistic reasoning"
    - "computer aided verification for mechanism design"
    - "advanced probabilistic couplings for differential privacy"

Connected through hyperedge HE-6 (induced by source paper "stochastic nonconvex optimization with large minibatches"):
  with members:
    - "memory and communication efficient distributed stochastic optimization with minibatch prox"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "incremental aggregated proximal and augmented lagrangian algorithms"
    - "adam a method for stochastic optimization"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "adadelta an adaptive learning rate method"
    - "convergence rates of inexact proximal gradient methods for convex optimization"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-7 (induced by source paper "variance reduced stochastic learning under random reshuffling"):
  with members:
    - "variance reduced stochastic learning by networked agents under random reshuffling"
    - "federated optimization distributed machine learning for on device intelligence"
    - "efficient distributed sgd with variance reduction"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "stop wasting my gradients practical svrg"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "memory and communication efficient distributed stochastic optimization with minibatch prox"):
  with members:
    - "incremental aggregated proximal and augmented lagrangian algorithms"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "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)