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 "revisiting large scale distributed machine learning"):
  Abstract: Nowadays, with the widespread of smartphones and other portable gadgets equipped with a variety of sensors, data is ubiquitous available and the focus of machine learning has shifted from being able to infer from small training samples to dealing with large scale high-dimensional data. In domains such as personal healthcare applications, which motivates this survey, distributed machine learning is a promising line of research, both for scaling up learning algorithms, but mostly for dealing with data which is inherently produced at different locations. This report offers a thorough overview of and state-of-the-art algorithms for distributed machine learning, for both supervised and unsupervised learning, ranging from simple linear logistic regression to graphical models and clustering. We propose future directions for most categories, specific to the potential personal healthcare applications. With this in mind, the report focuses on how security and low communication overhead can be assured in the specific case of a strictly client-server architectural model. As particular directions we provides an exhaustive presentation of an empirical clustering algorithm, k-windows, and proposed an asynchronous distributed machine learning algorithm that would scale well and also would be computationally cheap and easy to implement.

Includes members:
  Member "big data analytics in bioinformatics a machine learning perspective":
    also connected through other hyperedges:
      (none)
  Member "generalized product of experts for automatic and principled fusion of gaussian process predictions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "product kanerva machines factorized bayesian memory")
  Member "distributed parameter estimation via pseudo likelihood":
    also connected through other hyperedges:
      (none)
  Member "a reliable effective terascale linear learning system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stannis low power acceleration of deep neural network training using computational storage")

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)