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: "analysis of dawnbench a time to accuracy machine learning performance benchmark"
  Abstract: The deep learning community has proposed optimizations spanning hardware, software, and learning theory to improve the computational performance of deep learning workloads. While some of these optimizations perform the same operations faster (e.g., switching from a NVIDIA K80 to P100), many modify the semantics of the training procedure (e.g., large minibatch training, reduced precision), which can impact a model's generalization ability. Due to a lack of standard evaluation criteria that considers these trade-offs, it has become increasingly difficult to compare these different advances. To address this shortcoming, DAWNBENCH and the upcoming MLPERF benchmarks use time-to-accuracy as the primary metric for evaluation, with the accuracy threshold set close to state-of-the-art and measured on a held-out dataset not used in training; the goal is to train to this accuracy threshold as fast as possible. In DAWNBENCH , the winning entries improved time-to-accuracy on ImageNet by two orders of magnitude over the seed entries. Despite this progress, it is unclear how sensitive time-to-accuracy is to the chosen threshold as well as the variance between independent training runs, and how well models optimized for time-to-accuracy generalize. In this paper, we provide evidence to suggest that time-to-accuracy has a low coefficient of variance and that the models tuned for it generalize nearly as well as pre-trained models. We additionally analyze the winning entries to understand the source of these speedups, and give recommendations for future benchmarking efforts.

Connected through hyperedge HE-1 (induced by source paper "towards the systematic reporting of the energy and carbon footprints of machine learning"):
  with members:
    - "tackling climate change with machine learning"
    - "pay less attention with lightweight and dynamic convolutions"
    - "accelerating deep learning by focusing on the biggest losers"
    - "hardnet a low memory traffic network"
    - "energy and policy considerations for deep learning in nlp"
    - "quantifying the carbon emissions of machine learning"
    - "single headed attention rnn stop thinking with your head"
    - "green ai"

Connected through hyperedge HE-2 (induced by source paper "qutibench benchmarking neural networks on heterogeneous hardware"):
  with members:
    - "binareye an always on energy accuracy scalable binary cnn processor with all memory on chip in 28nm cmos"
    - "training and inference with integers in deep neural networks"
    - "tvm end to end optimization stack for deep learning"
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "bismo a scalable bit serial matrix multiplication overlay for reconfigurable computing"
    - "tbd benchmarking and analyzing deep neural network training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "toward computation and memory efficient neural network acoustic models with binary weights and activations"

Connected through hyperedge HE-3 (induced by source paper "mlperf training benchmark"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "scalable realistic recommendation datasets through fractal expansions"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "scalable methods for 8 bit training of neural networks"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "nvidia tensor core programmability performance precision"
    - "image classification at supercomputer scale"

Connected through hyperedge HE-4 (induced by source paper "benanza automatic mu benchmark generation to compute lower bound latency and inform optimizations of deep learning models on gpus"):
  with members:
    - "a survey on compiler autotuning using machine learning"
    - "miopen an open source library for deep learning primitives"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "xsp across stack profiling and analysis of machine learning models on gpus"
    - "neurovectorizer end to end vectorization with deep reinforcement learning"
    - "arcface additive angular margin loss for deep face recognition"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (induced by source paper "wield systematic reinforcement learning with progressive randomization"):
  with members:
    - "neo a learned query optimizer"
    - "challenges of real world reinforcement learning"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "deep reinforcement learning for join order enumeration"
    - "learning scheduling algorithms for data processing clusters"
    - "rlgraph modular computation graphs for deep reinforcement learning"
    - "unity a general platform for intelligent agents"

Connected through hyperedge HE-6 (induced by source paper "memory efficient adaptive optimization"):
  with members:
    - "introduction to online convex optimization"
    - "on the convergence of adam and beyond"
    - "shampoo preconditioned stochastic tensor optimization"
    - "regularized evolution for image classifier architecture search"
    - "the case for full matrix adaptive regularization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "benchmarking contemporary deep learning hardware and frameworks a survey of qualitative metrics"):
  with members:
    - "bigdl a distributed deep learning framework for big data"
    - "improving data quality through deep learning and statistical models"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "chainermn scalable distributed deep learning framework"
    - "deep learning on fpgas past present and future"
    - "theano a python framework for fast computation of mathematical expressions"
    - "tensorflow a system for large scale machine learning"
    - "blocks and fuel frameworks for deep learning"

Connected through hyperedge HE-8 (induced by source paper "the design and implementation of a scalable dl benchmarking platform"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "aibench an industry standard internet service ai benchmark suite"
    - "dynamic control flow in large scale machine learning"
    - "tbd benchmarking and analyzing deep neural network training"
    - "tensorflow serving flexible high performance ml serving"
    - "clipper a low latency online prediction serving system"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

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.LG(Machine Learning)