human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "avoiding communication in primal and dual block coordinate descent methods"
  Abstract: Primal and dual block coordinate descent methods are iterative methods for solving regularized and unregularized optimization problems. Distributed-memory parallel implementations of these methods have become popular in analyzing large machine learning datasets. However, existing implementations communicate at every iteration which, on modern data center and supercomputing architectures, often dominates the cost of floating-point computation. Recent results on communication-avoiding Krylov subspace methods suggest that large speedups are possible by re-organizing iterative algorithms to avoid communication. We show how applying similar algorithmic transformations can lead to primal and dual block coordinate descent methods that only communicate every $s$ iterations--where $s$ is a tuning parameter--instead of every iteration for the \textit{regularized least-squares problem}. We show that the communication-avoiding variants reduce the number of synchronizations by a factor of $s$ on distributed-memory parallel machines without altering the convergence rate and attains strong scaling speedups of up to $6.1\times$ on a Cray XC30 supercomputer.

Connected through hyperedge HE-1 (induced by source paper "recent developments in iterative methods for reducing synchronization"):
  with members:
    - "asynchronous time parallel method based on laplace transform"
    - "analyzing and improving maximal attainable accuracy in the communication hiding pipelined bicgstab method"
    - "the communication hiding pipelined bicgstab method for the parallel solution of large unsymmetric linear systems"
    - "asynchronous communications library for the parallel in time solution of black scholes equation"
    - "asynchronous parareal algorithm applied to european option pricing"

Connected through hyperedge HE-2 (induced by source paper "oversketch approximate matrix multiplication for the cloud"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "numpywren serverless linear algebra"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "occupy the cloud distributed computing for the 99"
    - "serverless computing current trends and open problems"
    - "a sequential approximation framework for coded distributed optimization"
    - "a practical guide to randomized matrix computations with matlab implementations"

Connected through hyperedge HE-3 (induced by source paper "avoiding synchronization in first order methods for sparse convex optimization"):
  with members:
    - "avoiding communication in proximal methods for convex optimization problems"

Question: Please tell me which class the center node 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)