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: "scalable stochastic alternating direction method of multipliers"
  Abstract: Stochastic alternating direction method of multipliers (ADMM), which visits only one sample or a mini-batch of samples each time, has recently been proved to achieve better performance than batch ADMM. However, most stochastic methods can only achieve a convergence rate $O(1/\sqrt T)$ on general convex problems,where T is the number of iterations. Hence, these methods are not scalable with respect to convergence rate (computation cost). There exists only one stochastic method, called SA-ADMM, which can achieve convergence rate $O(1/T)$ on general convex problems. However, an extra memory is needed for SA-ADMM to store the historic gradients on all samples, and thus it is not scalable with respect to storage cost. In this paper, we propose a novel method, called scalable stochastic ADMM(SCAS-ADMM), for large-scale optimization and learning problems. Without the need to store the historic gradients, SCAS-ADMM can achieve the same convergence rate $O(1/T)$ as the best stochastic method SA-ADMM and batch ADMM on general convex problems. Experiments on graph-guided fused lasso show that SCAS-ADMM can achieve state-of-the-art performance in real applications

Connected through hyperedge HE-1 (induced by source paper "accelerated variance reduced stochastic admm"):
  with members:
    - "fast and light stochastic admm"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "fast stochastic alternating direction method of multipliers"
    - "online alternating direction method"

Connected through hyperedge HE-2 (induced by source paper "temporal model adaptation for person re identification"):
  with members:
    - "learning to rank in person re identification with metric ensembles"
    - "hierarchical subquery evaluation for active learning on a graph"
    - "person re identification with correspondence structure learning"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "person re identification by local maximal occurrence representation and metric learning"
    - "a survey on metric learning for feature vectors and structured data"
    - "guaranteed classification via regularized similarity learning"

Connected through hyperedge HE-3 (induced by source paper "stochastic variance reduced admm"):
  with members:
    - "parallel algorithms for constrained tensor factorization via alternating direction method of multipliers"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "fast stochastic alternating direction method of multipliers"
    - "online alternating direction method"

Connected through hyperedge HE-4 (induced by source paper "fast and light stochastic admm"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "fast stochastic alternating direction method of multipliers"
    - "online alternating direction method"

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)