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: "performance characterization of in memory data analytics on a modern cloud server"
  Abstract: In last decade, data analytics have rapidly progressed from traditional disk-based processing to modern in-memory processing. However, little effort has been devoted at enhancing performance at micro-architecture level. This paper characterizes the performance of in-memory data analytics using Apache Spark framework. We use a single node NUMA machine and identify the bottlenecks hampering the scalability of workloads. We also quantify the inefficiencies at micro-architecture level for various data analysis workloads. Through empirical evaluation, we show that spark workloads do not scale linearly beyond twelve threads, due to work time inflation and thread level load imbalance. Further, at the micro-architecture level, we observe memory bound latency to be the major cause of work time inflation.

Connected through hyperedge HE-1 (induced by source paper "performance optimization for edge cloud serverless platforms via dynamic task placement"):
  with members:
    - "edgebench benchmarking edge computing platforms"
    - "serverless computing current trends and open problems"

Connected through hyperedge HE-2 (induced by source paper "processing data where it makes sense enabling in memory computation"):
  with members:
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "architectural techniques for improving nand flash memory reliability"
    - "what your dram power models are not telling you lessons from a detailed experimental study"
    - "techniques for shared resource management in systems with throughput processors"
    - "errors in flash memory based solid state drives analysis mitigation and recovery"
    - "the rowhammer problem and other issues we may face as memory becomes denser"
    - "error characterization mitigation and recovery in flash memory based solid state drives"
    - "using ecc dram to adaptively increase memory capacity"

Connected through hyperedge HE-3 (induced by source paper "near memory computing past present and future"):
  with members:
    - "memory and parallelism analysis using a platform independent approach"
    - "platform independent software analysis for near memory computing"
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "identifying the potential of near data computing for apache spark"
    - "near memory address translation"
    - "hitting the memory wall implications of the obvious"

Connected through hyperedge HE-4 (induced by source paper "platform independent software analysis for near memory computing"):
  with members:
    - "identifying the potential of near data computing for apache spark"

Connected through hyperedge HE-5 (induced by source paper "improving spark application throughput via memory aware task co location a mixture of experts approach"):
  with members:
    - "bigdatabench a big data benchmark suite from web search engines"
    - "mli an api for distributed machine learning"

Connected through hyperedge HE-6 (induced by source paper "architectural impact on performance of in memory data analytics apache spark case study"):
  with members:
    - "mllib machine learning in apache spark"
    - "bdgs a scalable big data generator suite in big data benchmarking"
    - "bigdatabench a big data benchmark suite from internet services"
    - "characterizing and subsetting big data workloads"
    - "characterizing data analysis workloads in data centers"

Connected through hyperedge HE-7 (induced by source paper "hpat high performance analytics with scripting ease of use"):
  with members:
    - "characterizing and subsetting big data workloads"

Connected through hyperedge HE-8 (induced by source paper "how data volume affects spark based data analytics on a scale up server"):
  with members:
    - "bdgs a scalable big data generator suite in big data benchmarking"
    - "bigdatabench a big data benchmark suite from internet services"
    - "characterizing and subsetting big data workloads"
    - "characterizing data analysis workloads in data centers"

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)