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: "the sage project a storage centric approach for exascale computing"
  Abstract: SAGE (Percipient StorAGe for Exascale Data Centric Computing) is a European Commission funded project towards the era of Exascale computing. Its goal is to design and implement a Big Data/Extreme Computing (BDEC) capable infrastructure with associated software stack. The SAGE system follows a "storage centric" approach as it is capable of storing and processing large data volumes at the Exascale regime. #R##N#SAGE addresses the convergence of Big Data Analysis and HPC in an era of next-generation data centric computing. This convergence is driven by the proliferation of massive data sources, such as large, dispersed scientific instruments and sensors where data needs to be processed, analyzed and integrated into simulations to derive scientific and innovative insights. A first prototype of the SAGE system has been been implemented and installed at the Julich Supercomputing Center. The SAGE storage system consists of multiple types of storage device technologies in a multi-tier I/O hierarchy, including flash, disk, and non-volatile memory technologies. The main SAGE software component is the Seagate Mero Object Storage that is accessible via the Clovis API and higher level interfaces. The SAGE project also includes scientific applications for the validation of the SAGE concepts. #R##N#The objective of this paper is to present the SAGE project concepts, the prototype of the SAGE platform and discuss the software architecture of the SAGE system.

Connected through hyperedge HE-1 (induced by source paper "multi gpu acceleration of the ipic3d implicit particle in cell code"):
  with members:
    - "tensorflow doing hpc"
    - "nvidia tensor core programmability performance precision"

Connected through hyperedge HE-2 (induced by source paper "characterizing deep learning i o workloads in tensorflow"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "exploring scientific application performance using large scale object storage"
    - "nvidia tensor core programmability performance precision"
    - "in datacenter performance analysis of a tensor processing unit"
    - "theano mpi a theano based distributed training framework"
    - "benchmarking state of the art deep learning software tools"
    - "exploring application performance on emerging hybrid memory supercomputers"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "exploring scientific application performance using large scale object storage"):
  with members:
    - "sage percipient storage for exascale data centric computing"
    - "mpi windows on storage for hpc applications"
    - "daos for extreme scale systems in scientific applications"
    - "exploring the performance benefit of hybrid memory system on hpc environments"
    - "exploring application performance on emerging hybrid memory supercomputers"

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)