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: "exploring application performance on emerging hybrid memory supercomputers"
  Abstract: Next-generation supercomputers will feature more hierarchical and heterogeneous memory systems with different memory technologies working side-by-side. A critical question is whether at large scale existing HPC applications and emerging data-analytics workloads will have performance improvement or degradation on these systems. We propose a systematic and fair methodology to identify the trend of application performance on emerging hybrid-memory systems. We model the memory system of next-generation supercomputers as a combination of "fast" and "slow" memories. We then analyze performance and dynamic execution characteristics of a variety of workloads, from traditional scientific applications to emerging data analytics to compare traditional and hybrid-memory systems. Our results show that data analytics applications can clearly benefit from the new system design, especially at large scale. Moreover, hybrid-memory systems do not penalize traditional scientific applications, which may also show performance improvement.

Connected through hyperedge HE-1 (induced by source paper "sage percipient storage for exascale data centric computing"):
  with members:
    - "mpi windows on storage for hpc applications"
    - "preparing hpc applications for the exascale era a decoupling strategy"
    - "mpi streams for hpc applications"
    - "savu a python based mpi framework for simultaneous processing of multiple n dimensional large tomography datasets"
    - "enabling highly scalable remote memory access programming with mpi 3 one sided"

Connected through hyperedge HE-2 (induced by source paper "system evaluation of the intel optane byte addressable nvm"):
  with members:
    - "basic performance measurements of the intel optane dc persistent memory module"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (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"
    - "the sage project a storage centric approach for exascale computing"
    - "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"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (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"
    - "the sage project a storage centric approach for exascale computing"
    - "daos for extreme scale systems in scientific applications"
    - "exploring the performance benefit of hybrid memory system on hpc environments"

Connected through hyperedge HE-5 (induced by source paper "mpi windows on storage for hpc applications"):
  with members:
    - "exploring the performance benefit of hybrid memory system on hpc environments"
    - "extending message passing interface windows to storage"
    - "enabling highly scalable remote memory access programming with mpi 3 one sided"

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)