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: "accelerated variance reduced stochastic admm"
  Abstract: Recently, many variance reduced stochastic alternating direction method of multipliers (ADMM) methods (e.g.\ SAG-ADMM, SDCA-ADMM and SVRG-ADMM) have made exciting progress such as linear convergence rates for strongly convex problems. However, the best known convergence rate for general convex problems is O(1/T) as opposed to O(1/T^2) of accelerated batch algorithms, where $T$ is the number of iterations. Thus, there still remains a gap in convergence rates between existing stochastic ADMM and batch algorithms. To bridge this gap, we introduce the momentum acceleration trick for batch optimization into the stochastic variance reduced gradient based ADMM (SVRG-ADMM), which leads to an accelerated (ASVRG-ADMM) method. Then we design two different momentum term update rules for strongly convex and general convex cases. We prove that ASVRG-ADMM converges linearly for strongly convex problems. Besides having a low per-iteration complexity as existing stochastic ADMM methods, ASVRG-ADMM improves the convergence rate on general convex problems from O(1/T) to O(1/T^2). Our experimental results show the effectiveness of ASVRG-ADMM.

Connected through hyperedge HE-1 (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"
    - "memory and communication efficient distributed stochastic optimization with minibatch prox"
    - "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-2 (induced by source paper "vr sgd a simple stochastic variance reduction method for machine learning"):
  with members:
    - "fast stochastic variance reduced gradient method with momentum acceleration for machine learning"
    - "nonconvex sparse learning via stochastic optimization with progressive variance reduction"
    - "variance reduced and projection free stochastic optimization"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "deep learning with elastic averaging sgd"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-3 (induced by source paper "larger is better the effect of learning rates enjoyed by stochastic optimization with progressive variance reduction"):
  with members:
    - "guaranteed sufficient decrease for variance reduced stochastic gradient descent"
    - "fast stochastic variance reduced gradient method with momentum acceleration for machine learning"
    - "nonconvex sparse learning via stochastic optimization with progressive variance reduction"
    - "variance reduced and projection free stochastic optimization"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"

Connected through hyperedge HE-4 (induced by source paper "fast stochastic variance reduced gradient method with momentum acceleration for machine learning"):
  with members:
    - "guaranteed sufficient decrease for variance reduced stochastic gradient descent"
    - "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"
    - "stochastic subgradient methods with linear convergence for polyhedral convex optimization"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

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)