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: "distributed stochastic optimization with gradient tracking over strongly connected networks"
  Abstract: In this paper, we study distributed stochastic optimization to minimize a sum of smooth and strongly-convex local cost functions over a network of agents, communicating over a strongly-connected graph. Assuming that each agent has access to a stochastic first-order oracle ($\mathcal{SFO}$), we propose a novel distributed method, called $\mathcal{S}$-$\mathcal{AB}$, where each agent uses an auxiliary variable to asymptotically track the gradient of the global cost in expectation. The $\mathcal{S}$-$\mathcal{AB}$ algorithm employs row- and column-stochastic weights simultaneously to ensure both consensus and optimality. Since doubly-stochastic weights are not used, $\mathcal{S}$-$\mathcal{AB}$ is applicable to arbitrary strongly-connected graphs. We show that under a sufficiently small constant step-size, $\mathcal{S}$-$\mathcal{AB}$ converges linearly (in expected mean-square sense) to a neighborhood of the global minimizer. We present numerical simulations based on real-world data sets to illustrate the theoretical results.

Connected through hyperedge HE-1 (induced by source paper "gradient tracking and variance reduction for decentralized optimization and machine learning"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "sgd general analysis and improved rates"
    - "on the performance of exact diffusion over adaptive networks"
    - "an exact quantized decentralized gradient descent algorithm"
    - "on the influence of bias correction on distributed stochastic optimization"
    - "asyspa an exact asynchronous algorithm for convex optimization over digraphs"
    - "stochastic gradient push for distributed deep learning"
    - "variance reduced stochastic learning by networked agents under random reshuffling"

Connected through hyperedge HE-2 (induced by source paper "decentralized stochastic gradient tracking for non convex empirical risk minimization"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "asynchronous decentralized optimization in directed networks"
    - "on the performance of exact diffusion over adaptive networks"
    - "decentralized deep learning with arbitrary communication compression"
    - "asynchronous subgradient push"
    - "communication compression for decentralized training"
    - "distributed learning over unreliable networks"
    - "asyspa an exact asynchronous algorithm for convex optimization over digraphs"

Connected through hyperedge HE-3 (induced by source paper "an introduction to decentralized stochastic optimization with gradient tracking"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "on the performance of exact diffusion over adaptive networks"
    - "an exact quantized decentralized gradient descent algorithm"
    - "on the influence of bias correction on distributed stochastic optimization"
    - "tutorial on dynamic average consensus the problem its applications and the algorithms"
    - "stochastic gradient push for distributed deep learning"
    - "variance reduced stochastic learning by networked agents under random reshuffling"
    - "variance reduced stochastic learning under random reshuffling"

Connected through hyperedge HE-4 (induced by source paper "distributed learning in non convex environments part ii polynomial escape from saddle points"):
  with members:
    - "on nonconvex optimization for machine learning gradients stochasticity and saddle points"
    - "escaping saddles with stochastic gradients"
    - "how to escape saddle points efficiently"
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "next in network nonconvex optimization"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"

Connected through hyperedge HE-5 (induced by source paper "decentralized stochastic gradient tracking for empirical risk minimization"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "asynchronous decentralized optimization in directed networks"
    - "on the influence of bias correction on distributed stochastic optimization"
    - "asynchronous subgradient push"
    - "distributed learning over unreliable networks"
    - "asyspa an exact asynchronous algorithm for convex optimization over digraphs"
    - "stochastic gradient push for distributed deep learning"
    - "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)