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: "matrix factorization at scale a comparison of scientific data analytics in spark and c mpi using three case studies"
  Abstract: We explore the trade-offs of performing linear algebra using Apache Spark, compared to traditional C and MPI implementations on HPC platforms. Spark is designed for data analytics on cluster computing platforms with access to local disks and is optimized for data-parallel tasks. We examine three widely-used and important matrix factorizations: NMF (for physical plausability), PCA (for its ubiquity) and CX (for data interpretability). We apply these methods to TB-sized problems in particle physics, climate modeling and bioimaging. The data matrices are tall-and-skinny which enable the algorithms to map conveniently into Spark's data-parallel model. We perform scaling experiments on up to 1600 Cray XC40 nodes, describe the sources of slowdowns, and provide tuning guidance to obtain high performance.

Connected through hyperedge HE-1 (induced by source paper "running alchemist on cray xc and cs series supercomputers dask and pyspark interfaces deployment options and data transfer times"):
  with members:
    - "alchemist an apache spark mpi interface"
    - "tune a research platform for distributed model selection and training"
    - "accelerating large scale data analysis by offloading to high performance computing libraries using alchemist"
    - "ray a distributed framework for emerging ai applications"
    - "rllib abstractions for distributed reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "accelerating large scale data analysis by offloading to high performance computing libraries using alchemist"):
  with members:
    - "alchemist an apache spark mpi interface"
    - "building near real time processing pipelines with the spark mpi platform"
    - "large scale kernel learning using block coordinate descent"

Connected through hyperedge HE-3 (induced by source paper "alchemist an apache spark mpi interface"):
  with members:
    - "building near real time processing pipelines with the spark mpi platform"
    - "accelerating large scale data analysis by offloading to high performance computing libraries using alchemist"

Connected through hyperedge HE-4 (induced by source paper "performance evaluation of an algorithm based asynchronous checkpoint restart fault tolerant application using mixed mpi gpi 2"):
  with members:
    - "building a fault tolerant application using the gaspi communication layer"
    - "a tale of two data intensive paradigms applications abstractions and architectures"

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)