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 "the scalability for parallel machine learning training algorithm dataset matters"):
  Abstract: To gain a better performance, many researchers put more computing resource into an application. However, in the AI area, there is still a lack of a successful large-scale machine learning training application: The scalability and performance reproducibility of parallel machine learning training algorithm are limited and there are a few pieces of research focusing on why these indexes are limited but there are very few research efforts explaining the reasons in essence. In this paper, we propose that the sample difference in dataset plays a more prominent role in parallel machine learning algorithm scalability. Dataset characters can measure sample difference. These characters include the variance of the sample in a dataset, sparsity, sample diversity and similarity in sampling sequence. To match our proposal, we choose four kinds of parallel machine learning training algorithms as our research objects: (1) Asynchronous parallel SGD algorithm (Hogwild! algorithm) (2) Parallel model average SGD algorithm (Mini-batch SGD algorithm) (3) Decenterilization optimization algorithm, (4) Dual Coordinate Optimization (DADM algorithm). These algorithms cover different types of machine learning optimization algorithms. We present the analysis of their convergence proof and design experiments. Our results show that the characters datasets decide the scalability of the machine learning algorithm. What is more, there is an upper bound of parallel scalability for machine learning algorithms.

Includes members:
  Member "decentralization meets quantization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gradient descent with compressed iterates")
  Member "exascale deep learning for climate analytics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tackling climate change with machine learning")
  Member "imagenet training in minutes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "scaling sgd batch size to 32k for imagenet training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "a general distributed dual coordinate optimization framework for regularized loss minimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "solving empirical risk minimization in the current matrix multiplication time")
  Member "distributed mean estimation with limited communication":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rpn a residual pooling network for efficient federated learning")
  Member "adding vs averaging in distributed primal dual optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "faster on device training using new federated momentum algorithm")
  Member "distributed optimization with arbitrary local solvers":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adaptivity of stochastic gradient methods for nonconvex optimization")

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.DC(Distributed, Parallel, and Cluster Computing)