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: "efficient processing of deep neural networks a tutorial and survey"
  Abstract: Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems. #R##N#This article aims to provide a comprehensive tutorial and survey about the recent advances towards the goal of enabling efficient processing of DNNs. Specifically, it will provide an overview of DNNs, discuss various hardware platforms and architectures that support DNNs, and highlight key trends in reducing the computation cost of DNNs either solely via hardware design changes or via joint hardware design and DNN algorithm changes. It will also summarize various development resources that enable researchers and practitioners to quickly get started in this field, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic co-designs, being proposed in academia and industry. #R##N#The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the trade-offs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities.

Connected through hyperedge HE-1 (induced by source paper "searching for winograd aware quantized networks"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "winograd convolution for dnns beyond linear polynomials"
    - "visual wake words dataset"
    - "sparse sparse architecture search for cnns on resource constrained microcontrollers"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "searching for mobilenetv3"
    - "efficient winograd convolution via integer arithmetic"
    - "differentiable soft quantization bridging full precision and low bit neural networks"

Connected through hyperedge HE-2 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "bigdl a distributed deep learning framework for big data"
    - "network decoupling from regular to depthwise separable convolutions"
    - "deep neural network compression with single and multiple level quantization"

Connected through hyperedge HE-3 (induced by source paper "propagating asymptotic estimated gradients for low bitwidth quantized neural networks"):
  with members:
    - "precision highway for ultra low precision quantization"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "relaxed quantization for discretized neural networks"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "pact parameterized clipping activation for quantized neural networks"
    - "towards accurate binary convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "what is the state of neural network pruning"):
  with members:
    - "jointly sparse convolutional neural networks in dual spatial winograd domains"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "benchmark analysis of representative deep neural network architectures"
    - "sprintz time series compression for the internet of things"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "efficient neural audio synthesis"
    - "network compression using correlation analysis of layer responses"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"

Connected through hyperedge HE-5 (induced by source paper "reactnet towards precise binary neural network with generalized activation functions"):
  with members:
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "regularizing activation distribution for training binarized deep networks"
    - "accurate and compact convolutional neural networks with trained binarization"
    - "xnor net improved binary neural networks"
    - "dabnn a super fast inference framework for binary neural networks on arm devices"
    - "global sparse momentum sgd for pruning very deep neural networks"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-6 (induced by source paper "quantized neural network inference with precision batching"):
  with members:
    - "learning fast algorithms for linear transforms using butterfly factorizations"
    - "quantized reinforcement learning quarl"
    - "adaptivfloat a floating point based data type for resilient deep learning inference"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "alternating multi bit quantization for recurrent neural networks"
    - "word2bits quantized word vectors"
    - "pact parameterized clipping activation for quantized neural networks"
    - "on the state of the art of evaluation in neural language models"

Connected through hyperedge HE-7 (induced by source paper "ternary compression for communication efficient federated learning"):
  with members:
    - "a survey on federated learning systems vision hype and reality for data privacy and protection"
    - "towards federated learning at scale system design"
    - "robust and communication efficient federated learning from non iid data"
    - "federated machine learning concept and applications"
    - "federated learning in mobile edge networks a comprehensive survey"
    - "model compression via distillation and quantization"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "multi objective evolutionary federated learning"

Connected through hyperedge HE-8 (induced by source paper "flexibo cost aware multi objective optimization of deep neural networks"):
  with members:
    - "transfer learning for performance modeling of deep neural network systems"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "learning software configuration spaces a systematic literature review"
    - "multi objective bayesian optimisation with preferences over objectives"
    - "a flexible framework for multi objective bayesian optimization using random scalarizations"
    - "ordered preference elicitation strategies for supporting multi objective decision making"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "proxylessnas direct neural architecture search on target task and hardware"

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.CV(Computer Vision and Pattern Recognition)