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 "matrix factorization on gpus with memory optimization and approximate computing"):
  Abstract: Matrix factorization (MF) discovers latent features from observations, which has shown great promises in the fields of collaborative filtering, data compression, feature extraction, word embedding, etc. While many problem-specific optimization techniques have been proposed, alternating least square (ALS) remains popular due to its general applicability e.g. easy to handle positive-unlabeled inputs, fast convergence and parallelization capability. Current MF implementations are either optimized for a single machine or with a need of a large computer cluster but still are insufficient. This is because a single machine provides limited compute power for large-scale data while multiple machines suffer from the network communication bottleneck. #R##N#To address the aforementioned challenge, accelerating ALS on graphics processing units (GPUs) is a promising direction. We propose the novel approach in enhancing the MF efficiency via both memory optimization and approximate computing. The former exploits GPU memory hierarchy to increase data reuse, while the later reduces unnecessary computing without hurting the convergence of learning algorithms. Extensive experiments on large-scale datasets show that our solution not only outperforms the competing CPU solutions by a large margin but also has a 2x-4x performance gain compared to the state-of-the-art GPU solutions. Our implementations are open-sourced and publicly available.

Includes members:
  Member "cumf_sgd fast and scalable matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graphr accelerating graph processing using reram")
  Member "faster and cheaper parallelizing large scale matrix factorization on gpus":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "large scale collaborative filtering with product embeddings")
  Member "mllib machine learning in apache spark":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi layer optimizations for end to end data analytics")
  Member "factorbird a parameter server approach to distributed matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "federated clustering via matrix factorization models from model averaging to gradient sharing")
  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 "do deep nets really need to be deep":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost")

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)