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: "incremental aggregated proximal and augmented lagrangian algorithms"
  Abstract: We consider minimization of the sum of a large number of convex functions, and we propose an incremental aggregated version of the proximal algorithm, which bears similarity to the incremental aggregated gradient and subgradient methods that have received a lot of recent attention. Under cost function dierentiability and strong convexity assumptions, we show linear convergence for a suciently small constant stepsize. This result also applies to distributed asynchronous variants of the method, involving bounded interprocessor communication delays. We then consider dual versions of incremental proximal algorithms, which are incremental augmented Lagrangian methods for separable equality-constrained optimization problems. Contrary to the standard augmented Lagrangian method, these methods admit decomposition in the minimization of the augmented Lagrangian, and update the multipliers far more frequently. Our incremental aggregated augmented Lagrangian methods bear similarity to several known decomposition algorithms, most of which, however, are not incremental in nature: the augmented Lagrangian decomposition algorithm of Stephanopoulos and Westerberg [StW75], and the related methods of Tadjewski [Tad89] and Ruszczynski [Rus95], and the alternating direction method of multipliers (ADMM) and more recent variations. We compare these methods in terms of their properties, and highlight their potential advantages and limitations. We also address the solution of separable inequality-constrained optimization problems through the use of nonquadratic augmented Lagrangians such as the exponential, and we dually consider a corresponding incremental aggregated version of the proximal algorithm that uses nonquadratic regularization, such as an entropy function. We nally propose a closely related linearly convergent method for minimization of large dierentiable sums subject to an orthant constraint, which may be viewed as an incremental aggregated

Connected through hyperedge HE-1 (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"
    - "without replacement sampling for stochastic gradient methods convergence results and application to distributed optimization"
    - "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-2 (induced by source paper "memory and communication efficient distributed stochastic optimization with minibatch prox"):
  with members:
    - "without replacement sampling for stochastic gradient methods convergence results and application to distributed optimization"
    - "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.SY(Systems and Control)