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 non convex empirical risk minimization"):
  Abstract: This paper studies a decentralized stochastic gradient tracking (DSGT) algorithm for a non-convex empirical risk minimization problem over a peer-to-peer network, which is in sharp contrast to the existing DSGT works only for the convex problem. To handle the variance among decentralized datasets, the mini-batch in each node of the network is designed to be proportional to the size of its local dataset. We explicitly evaluate the convergence rate of DSGT in terms of algebraic connectivity of the network, mini-batch size, and learning rate. Importantly, our theoretical rate has an optimal dependence on the algebraic connectivity and can exactly recover the rate of the centralized stochastic gradient method. Moreover, we demonstrate that DSGT could achieve a linear speedup while a sublinear speedup is also possible, depending on the problem at hand. Numerical experiments for neural networks and logistic regression problems on CIFAR-10 finally illustrate the advantages of DSGT for decentralized training.

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 performance of exact diffusion over adaptive networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gradient tracking and variance reduction for decentralized optimization and machine learning")
  Member "decentralized deep learning with arbitrary communication compression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "trends and advancements in deep neural network communication")
  Member "asynchronous subgradient push":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "asynchronous policy evaluation in distributed reinforcement learning over networks")
  Member "communication compression for decentralized training":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic sign sgd for federated learning with theoretical guarantees")
  Member "distributed learning over unreliable networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "moniqua modulo quantized communication in decentralized sgd")

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)