human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "decentralized stochastic gradient tracking for empirical risk minimization"):
  Abstract: Recent works have shown superiorities of decentralized SGD to centralized counterparts in large-scale machine learning, but their theoretical gap is still not fully understood. In this paper, we propose a decentralized stochastic gradient tracking (DSGT) algorithm over peer-to-peer networks for empirical risk minimization problems, and explicitly evaluate its convergence rate in terms of key parameters of the problem, e.g., algebraic connectivity of the communication network, mini-batch size, and gradient variance. Importantly, it is the first theoretical result that can \emph{exactly} recover the rate of the centralized SGD, and has optimal dependence on the algebraic connectivity of the networks when using stochastic gradients. Moreover, we explicitly quantify how the network affects speedup and the rate improvement over existing works. Interestingly, we also point out for the first time that both linear and sublinear speedup can be possible. We empirically validate DSGT on neural networks and logistic regression problems, and show its advantage over the state-of-the-art algorithms.

Includes members:
  Member "decentralized stochastic optimization and gossip algorithms with compressed communication":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trends and advancements in deep neural network communication")
  Member "distributed stochastic optimization with gradient tracking over strongly connected networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gradient tracking and variance reduction for decentralized optimization and machine learning")
  Member "asynchronous decentralized optimization in directed networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "asynchronous policy evaluation in distributed reinforcement learning over networks")
  Member "on the influence of bias correction on distributed stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gradient tracking and variance reduction for decentralized optimization and machine learning")
  Member "asynchronous subgradient push":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "asynchronous policy evaluation in distributed reinforcement learning over networks")
  Member "distributed learning over unreliable networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "moniqua modulo quantized communication in decentralized sgd")
  Member "asyspa an exact asynchronous algorithm for convex optimization over digraphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "asynchronous policy evaluation in distributed reinforcement learning over networks")
  Member "stochastic gradient push for distributed deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gradient tracking and variance reduction for decentralized optimization and machine learning")

Question: Please tell me which class the source paper 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)