human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "data motif based proxy benchmarks for big data and ai workloads"):
  Abstract: For the architecture community, reasonable simulation time is a strong requirement in addition to performance data accuracy. However, emerging big data and AI workloads are too huge at binary size level and prohibitively expensive to run on cycle-accurate simulators. The concept of data motif, which is identified as a class of units of computation performed on initial or intermediate data, is the first step towards building proxy benchmark to mimic the real-world big data and AI workloads. However, there is no practical way to construct a proxy benchmark based on the data motifs to help simulation-based research. In this paper, we embark on a study to bridge the gap between data motif and a practical proxy benchmark. We propose a data motif-based proxy benchmark generating methodology by means of machine learning method, which combine data motifs with different weights to mimic the big data and AI workloads. Furthermore, we implement various data motifs using light-weight stacks and apply the methodology to five real-world workloads to construct a suite of proxy benchmarks, considering the data types, patterns, and distributions. The evaluation results show that our proxy benchmarks shorten the execution time by 100s times on real systems while maintaining the average system and micro-architecture performance data accuracy above 90%, even changing the input data sets or cluster configurations. Moreover, the generated proxy benchmarks reflect consistent performance trends across different architectures. To facilitate the community, we will release the proxy benchmarks on the project homepage this http URL

Includes members:
  Member "bigdatabench a scalable and unified big data and ai benchmark suite":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "data motifs a lens towards fully understanding big data and ai workloads":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "fathom reference workloads for modern deep learning methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "rethinking the inception architecture for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial ranking attack and defense")
  Member "the gap benchmark suite":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a parallel sparse tensor benchmark suite on cpus and gpus")
  Member "bdgs a scalable big data generator suite in big data benchmarking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "bigdatabench a big data benchmark suite from internet services":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tlb and pagewalk performance in multicore architectures with large die stacked dram cache")
  Member "characterizing and subsetting big data workloads":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "data motifs a lens towards fully understanding big data and ai workloads")

Question: Please tell me which class the source paper 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)