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 "robust and communication efficient collaborative learning"):
  Abstract: We consider a decentralized learning problem, where a set of computing nodes aim at solving a non-convex optimization problem collaboratively. It is well-known that decentralized optimization schemes face two major system bottlenecks: stragglers' delay and communication overhead. In this paper, we tackle these bottlenecks by proposing a novel decentralized and gradient-based optimization algorithm named as QuanTimed-DSGD. Our algorithm stands on two main ideas: (i) we impose a deadline on the local gradient computations of each node at each iteration of the algorithm, and (ii) the nodes exchange quantized versions of their local models. The first idea robustifies to straggling nodes and the second alleviates communication efficiency. The key technical contribution of our work is to prove that with non-vanishing noises for quantization and stochastic gradients, the proposed method exactly converges to the global optimal for convex loss functions, and finds a first-order stationary point in non-convex scenarios. Our numerical evaluations of the QuanTimed-DSGD on training benchmark datasets, MNIST and CIFAR-10, demonstrate speedups of up to 3x in run-time, compared to state-of-the-art decentralized optimization methods.

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 "an exact quantized decentralized gradient descent algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gradient tracking and variance reduction for decentralized optimization and machine learning")
  Member "speeding up distributed machine learning using codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "quantized decentralized consensus optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "decentralized deep learning with arbitrary communication compression")
  Member "signsgd compressed optimisation for non convex problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "compressed distributed gradient descent communication efficient consensus over networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fedpaq a communication efficient federated learning method with periodic averaging and quantization")
  Member "coded computation over heterogeneous clusters":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "polynomial codes an optimal design for high dimensional coded matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")

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)