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 "benchmarking deep learning hardware and frameworks qualitative metrics"):
  Abstract: Previous survey papers offer knowledge of deep learning hardware devices and software frameworks. This paper introduces benchmarking principles, surveys machine learning devices including GPUs, FPGAs, and ASICs, and reviews deep learning software frameworks. It also reviews these technologies with respect to benchmarking from the angles of our 7-metric approach to frameworks and 12-metric approach to hardware platforms. #R##N#After reading the paper, the audience will understand seven benchmarking principles, generally know that differential characteristics of mainstream AI devices, qualitatively compare deep learning hardware through our 12-metric approach for benchmarking hardware, and read benchmarking results of 16 deep learning frameworks via our 7-metric set for benchmarking frameworks.

Includes members:
  Member "bigdl a distributed deep learning framework for big data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications")
  Member "improving data quality through deep learning and statistical models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding artificial intelligence ethics and safety")
  Member "chainermn scalable distributed deep learning framework":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep leakage from gradients")
  Member "deep learning on fpgas past present and future":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "poet bin power efficient tiny binary neurons")
  Member "theano a python framework for fast computation of mathematical expressions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "blocks and fuel frameworks for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "benchmarking contemporary deep learning hardware and frameworks a survey of qualitative metrics")
  Member "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "purine a bi graph based deep learning framework":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "benchmarking contemporary deep learning hardware and frameworks a survey of qualitative metrics")

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)