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 "benchcouncil s view on benchmarking ai and other emerging workloads"):
  Abstract: This paper outlines BenchCounci's view on the challenges, rules, and vision of benchmarking modern workloads like Big Data, AI or machine learning, and Internet Services. We conclude the challenges of benchmarking modern workloads as FIDSS (Fragmented, Isolated, Dynamic, Service-based, and Stochastic), and propose the PRDAERS benchmarking rules that the benchmarks should be specified in a paper-and-pencil manner, relevant, diverse, containing different levels of abstractions, specifying the evaluation metrics and methodology, repeatable, and scaleable. We believe proposing simple but elegant abstractions that help achieve both efficiency and general-purpose is the final target of benchmarking in future, which may be not pressing. In the light of this vision, we shortly discuss BenchCouncil's related projects.

Includes members:
  Member "aibench an industry standard internet service ai benchmark suite":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mlmodelscope a distributed platform for ml model evaluation and benchmarking at scale")
  Member "mlperf inference benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deeprecsys a system for optimizing end to end at scale neural recommendation inference")
  Member "edge aibench towards comprehensive end to end edge computing benchmarking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ai oriented medical workload allocation for hierarchical cloud edge device computing")
  Member "mlperf training benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "data motif based proxy benchmarks for big data and ai workloads":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "characterizing deep learning training workloads on alibaba pai")
  Member "benchmark analysis of representative deep neural network architectures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "what is the state of neural network pruning")
  Member "bigdatabench a scalable and unified big data and ai benchmark suite":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")

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.PF(Performance)