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: "a survey and measurement study of gpu dvfs on energy conservation"
  Abstract: Energy efficiency has become one of the top design criteria for current computing systems. The dynamic voltage and frequency scaling (DVFS) has been widely adopted by laptop computers, servers, and mobile devices to conserve energy, while the GPU DVFS is still at a certain early age. This paper aims at exploring the impact of GPU DVFS on the application performance and power consumption, and furthermore, on energy conservation. We survey the state-of-the-art GPU DVFS characterizations, and then summarize recent research works on GPU power and performance models. We also conduct real GPU DVFS experiments on NVIDIA Fermi and Maxwell GPUs. According to our experimental results, GPU DVFS has significant potential for energy saving. The effect of scaling core voltage/frequency and memory voltage/frequency depends on not only the GPU architectures, but also the characteristic of GPU applications.

Connected through hyperedge HE-1 (induced by source paper "the impact of gpu dvfs on the energy and performance of deep learning an empirical study"):
  with members:
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "imagenet training in minutes"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "adacomp adaptive residual gradient compression for data parallel distributed training"
    - "gpgpu performance estimation with core and memory frequency scaling"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "performance modeling and evaluation of distributed deep learning frameworks on gpus"
    - "neuralpower predict and deploy energy efficient convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "performance and power evaluation of ai accelerators for training deep learning models"):
  with members:
    - "automl a survey of the state of the art"
    - "the impact of gpu dvfs on the energy and performance of deep learning an empirical study"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "image classification at supercomputer scale"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "in datacenter performance analysis of a tensor processing unit"
    - "gpgpu performance estimation with core and memory frequency scaling"

Connected through hyperedge HE-3 (induced by source paper "benchmarking the performance and power of ai accelerators for ai training"):
  with members:
    - "automl a survey of the state of the art"
    - "the impact of gpu dvfs on the energy and performance of deep learning an empirical study"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "image classification at supercomputer scale"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "gpgpu performance estimation with core and memory frequency scaling"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "energy efficient radio resource allocation for federated edge learning"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "signsgd compressed optimisation for non convex problems"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-5 (induced by source paper "optimization of hybrid parallel application execution in heterogeneous high performance computing systems considering execution time and power consumption"):
  with members:
    - "imagenet training in minutes"
    - "billion scale similarity search with gpus"
    - "reinforcement learning through asynchronous advantage actor critic on a gpu"
    - "caffe con troll shallow ideas to speed up deep learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "energy aware task partitioning on heterogeneous multiprocessor platforms"
    - "gridsim a toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing"

Connected through hyperedge HE-6 (induced by source paper "gpgpu performance estimation with core and memory frequency scaling"):
  with members:
    - "benchmarking state of the art deep learning software tools"
    - "dissecting gpu memory hierarchy through microbenchmarking"
    - "caffe convolutional architecture for fast feature embedding"

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)