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 the performance and power of ai accelerators for ai training"):
  Abstract: Deep learning has become widely used in complex AI applications. Yet, training a deep neural network (DNNs) model requires a huge amount of calculations, taking a long running time and consuming a lot of energy. Nowadays, many-core AI accelerators (e.g., GPUs and TPUs) are designed to improve the AI training performance. However, different processors from different vendors perform very differently in terms of performance and power consumption. To investigate the differences among several popular off-the-shelf processors (i.e., Intel CPU, NVIDIA GPU, AMD GPU and Google TPU) in training DNNs, we carry out a detailed benchmark study on the performance and power (when possible) of these processors when training a representative set of DNNs, including three classical convolutional neural networks (CNNs), a recurrent neural network (LSTM), Deep Speech 2, and Transformer. We try to understand the impact of hardware, vendor's software library, and deep learning framework on the final training performance. Our evaluation results make two valuable directions for end-users and vendors. For the end-users, the evaluation results provide a guide for selecting a proper accelerator for training DNN models. For the vendors, some advantages and disadvantages revealed in our evaluation results could be useful for future architecture design and software library optimization.

Includes members:
  Member "automl a survey of the state of the art":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "benchmark and survey of automated machine learning frameworks")
  Member "the impact of gpu dvfs on the energy and performance of deep learning an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "performance and power evaluation of ai accelerators for training deep learning models")
  Member "benchmarking tpu gpu and cpu platforms for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "image classification at supercomputer scale":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "intel ngraph an intermediate representation compiler and executor for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "highly scalable deep learning training system with mixed precision training imagenet in four minutes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "gpgpu performance estimation with core and memory frequency scaling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the impact of gpu dvfs on the energy and performance of deep learning an empirical study")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)