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: "faster and cheaper parallelizing large scale matrix factorization on gpus"
  Abstract: Matrix factorization (MF) is employed by many popular algorithms, e.g., collaborative filtering. The emerging GPU technology, with massively multicore and high intra-chip memory bandwidth but limited memory capacity, presents an opportunity for accelerating MF much further when appropriately exploiting the GPU architectural characteristics. #R##N#This paper presents cuMF, a CUDA-based matrix factorization library that implements memory-optimized alternate least square (ALS) method to solve very large-scale MF. CuMF uses a variety set of techniques to maximize the performance on either single or multiple GPUs. These techniques include smart access of sparse data leveraging GPU memory hierarchy, using data parallelism in conjunction with model parallelism, minimizing the communication overhead between computing units, and utilizing a novel topology-aware parallel reduction scheme. #R##N#With only a single machine with four Nvidia GPU cards, cuMF can be 6-10 times as fast, and 33-100 times as cost-efficient, compared with the state-of-art distributed CPU solutions. Moreover, this cuMF can solve the largest matrix factorization problem ever reported yet in current literature, while maintaining impressively good performance.

Connected through hyperedge HE-1 (induced by source paper "large scale collaborative filtering with product embeddings"):
  with members:
    - "deep sets"
    - "structured attention networks"
    - "starspace embed all the things"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "deep cross network for ad click predictions"
    - "specializing joint representations for the task of product recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "matrix factorization on gpus with memory optimization and approximate computing"):
  with members:
    - "cumf_sgd fast and scalable matrix factorization"
    - "mllib machine learning in apache spark"
    - "factorbird a parameter server approach to distributed matrix factorization"
    - "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-3 (induced by source paper "did distributed incremental block coordinate descent for nonnegative matrix factorization"):
  with members:
    - "a high performance parallel algorithm for nonnegative matrix factorization"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-4 (induced by source paper "cumf_sgd fast and scalable matrix factorization"):
  with members:
    - "a generic inverted index framework for similarity search on the gpu technical report"
    - "mllib machine learning in apache spark"
    - "factorbird a parameter server approach to distributed matrix factorization"
    - "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"

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)