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: "a high performance parallel algorithm for 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 software to solve the problem for big datasets. Existing distributed-memory algorithms are limited in terms of performance and applicability, as they are implemented using Hadoop and are designed only for sparse matrices. #R##N#We propose a distributed-memory parallel algorithm that computes the factorization by iteratively solving alternating non-negative least squares (NLS) subproblems for $W$ and $H$. To our knowledge, our algorithm is the first high-performance parallel algorithm for NMF. 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). As opposed to previous implementations, our algorithm is also flexible: (1) it performs well for dense and sparse matrices, and (2) it allows the user to choose from among multiple algorithms for solving local NLS subproblems within the alternating iterations. We demonstrate the scalability of our algorithm and compare it with baseline implementations, showing significant performance improvements.

Connected through hyperedge HE-1 (induced by source paper "fast convolutive nonnegative matrix factorization through coordinate and block coordinate updates"):
  with members:
    - "julia a fresh approach to numerical computing"
    - "computing a nonnegative matrix factorization provably"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-2 (induced by source paper "did distributed incremental block coordinate descent for nonnegative matrix factorization"):
  with members:
    - "faster and cheaper parallelizing large scale matrix factorization on gpus"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-3 (induced by source paper "design principles for sparse matrix multiplication on the gpu"):
  with members:
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-4 (induced by source paper "an efficient algorithm for non negative matrix factorization with random projections"):
  with members:
    - "compressed nonnegative matrix factorization is fast and accurate"

Connected through hyperedge HE-5 (induced by source paper "matrix factorization at scale a comparison of scientific data analytics in spark and c mpi using three case studies"):
  with members:
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
    - "mllib machine learning in apache spark"
    - "matrix computations and optimization in apache spark"
    - "scalable methods for nonnegative matrix factorizations of near separable tall and skinny matrices"
    - "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"
    - "randomized algorithms for matrices and data"
    - "block locally optimal preconditioned eigenvalue xolvers blopex in hypre and petsc"
    - "relative error cur matrix decompositions"

Connected through hyperedge HE-6 (induced by source paper "mpi faun an mpi based framework for alternating updating nonnegative matrix factorization"):
  with members:
    - "mllib machine learning in apache spark"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "scalable methods for nonnegative matrix factorizations of near separable tall and skinny matrices"
    - "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"
    - "tensor decompositions for learning latent variable models"
    - "descent methods for nonnegative matrix factorization"
    - "non negative matrix factorization with sparseness constraints"

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)