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 the performance benefit of hybrid memory system on hpc environments"
  Abstract: Hardware accelerators have become a de-facto standard to achieve high performance on current supercomputers and there are indications that this trend will increase in the future. Modern accelerators feature high-bandwidth memory next to the computing cores. For example, the Intel Knights Landing (KNL) processor is equipped with 16 GB of high-bandwidth memory (HBM) that works together with conventional DRAM memory. Theoretically, HBM can provide ∼4× higher bandwidth than conventional DRAM. However, many factors impact the effective performance achieved by applications, including the application memory access pattern, the problem size, the threading level and the actual memory configuration. In this paper, we analyze the Intel KNL system and quantify the impact of the most important factors on the application performance by using a set of applications that are representative of scientific and data-analytics workloads. Our results show that applications with regular memory access benefit from MCDRAM, achieving up to 3× performance when compared to the performance obtained using only DRAM. On the contrary, applications with random memory access pattern are latency-bound and may suffer from performance degradation when using only MCDRAM. For those applications, the use of additional hardware threads may help hide latency and achieve higher aggregated bandwidth when using HBM.

Connected through hyperedge HE-1 (induced by source paper "performance impact of memory channels on sparse and irregular algorithms"):
  with members:
    - "programming strategies for irregular algorithms on the emu chick"
    - "gunrock gpu graph analytics"
    - "mathematical foundations of the graphblas"
    - "the gap benchmark suite"

Connected through hyperedge HE-2 (induced by source paper "streambox hbm stream analytics on high bandwidth hybrid memory"):
  with members:
    - "scaling deep learning on gpu and knights landing clusters"
    - "fast sorting algorithms using avx 512 on intel knights landing"
    - "bigdatabench a big data benchmark suite from internet services"
    - "massively parallel sort merge joins in main memory multi core database systems"

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"
    - "the sage project a storage centric approach for exascale computing"
    - "daos for extreme scale systems in scientific applications"
    - "exploring application performance on emerging hybrid memory supercomputers"

Connected through hyperedge HE-4 (induced by source paper "the sage project a storage centric approach for exascale computing"):
  with members:
    - "mpi windows on storage for hpc applications"
    - "preparing hpc applications for the exascale era a decoupling strategy"
    - "extending message passing interface windows to storage"
    - "savu a python based mpi framework for simultaneous processing of multiple n dimensional large tomography datasets"

Connected through hyperedge HE-5 (induced by source paper "mpi windows on storage for hpc applications"):
  with members:
    - "extending message passing interface windows to storage"
    - "exploring application performance on emerging hybrid memory supercomputers"
    - "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)