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 "mpi faun an mpi based framework for alternating updating nonnegative matrix factorization"):
  Abstract: Non-negative matrix factorization (NMF) is the problem of determining two non-negative low rank factors $W$ and $H$, for the given input matrix $A$, such that $A \approx W H$. NMF is a useful tool for many applications in different domains such as topic modeling in text mining, background separation in video analysis, and community detection in social networks. Despite its popularity in the data mining community, there is a lack of efficient parallel algorithms to solve the problem for big data sets. #R##N#The main contribution of this work is a new, high-performance parallel computational framework for a broad class of NMF algorithms that iteratively solves alternating non-negative least squares (NLS) subproblems for $W$ and $H$. It maintains the data and factor matrices in memory (distributed across processors), uses MPI for interprocessor communication, and, in the dense case, provably minimizes communication costs (under mild assumptions). The framework is flexible and able to leverage a variety of NMF and NLS algorithms, including Multiplicative Update, Hierarchical Alternating Least Squares, and Block Principal Pivoting. Our implementation allows us to benchmark and compare different algorithms on massive dense and sparse data matrices of size that spans for few hundreds of millions to billions. We demonstrate the scalability of our algorithm and compare it with baseline implementations, showing significant performance improvements. The code and the datasets used for conducting the experiments are available online.

Includes members:
  Member "a high performance parallel algorithm for nonnegative matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast convolutive nonnegative matrix factorization through coordinate and block coordinate updates")
  Member "mllib machine learning in apache spark":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi layer optimizations for end to end data analytics")
  Member "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "moving objects detection with a moving camera a comprehensive review")
  Member "scalable methods for nonnegative matrix factorizations of near separable tall and skinny matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions")
  Member "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "machine learning on volatile instances")
  Member "tensor decompositions for learning latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")
  Member "descent methods for nonnegative matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convergence to second order stationarity for non negative matrix factorization provably and concurrently")
  Member "non negative matrix factorization with sparseness constraints":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "disentangling overlapping beliefs by structured matrix factorization")

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)