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: "eie efficient inference engine on compressed deep neural network"
  Abstract: State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets. While custom hardware helps the computation, fetching weights from DRAM is two orders of magnitude more expensive than ALU operations, and dominates the required power. #R##N#Previously proposed 'Deep Compression' makes it possible to fit large DNNs (AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by pruning the redundant connections and having multiple connections share the same weight. We propose an energy efficient inference engine (EIE) that performs inference on this compressed network model and accelerates the resulting sparse matrix-vector multiplication with weight sharing. Going from DRAM to SRAM gives EIE 120x energy saving; Exploiting sparsity saves 10x; Weight sharing gives 8x; Skipping zero activations from ReLU saves another 3x. Evaluated on nine DNN benchmarks, EIE is 189x and 13x faster when compared to CPU and GPU implementations of the same DNN without compression. EIE has a processing power of 102GOPS/s working directly on a compressed network, corresponding to 3TOPS/s on an uncompressed network, and processes FC layers of AlexNet at 1.88x10^4 frames/sec with a power dissipation of only 600mW. It is 24,000x and 3,400x more energy efficient than a CPU and GPU respectively. Compared with DaDianNao, EIE has 2.9x, 19x and 3x better throughput, energy efficiency and area efficiency.

Connected through hyperedge HE-1 (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-2 (induced by source paper "least squares binary quantization of neural networks"):
  with members:
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-3 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on the dimensionality of word embedding"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning transferable architectures for scalable image recognition"
    - "attention is all you need"
    - "multilingual hierarchical attention networks for document classification"
    - "a read write memory network for movie story understanding"

Connected through hyperedge HE-4 (induced by source paper "good subnetworks provably exist pruning via greedy forward selection"):
  with members:
    - "proving the lottery ticket hypothesis pruning is all you need"
    - "pruning from scratch"
    - "sipping neural networks sensitivity informed provable pruning of neural networks"
    - "searching for mobilenetv3"
    - "data independent neural pruning via coresets"
    - "legr filter pruning via learned global ranking"
    - "what s hidden in a randomly weighted neural network"
    - "provable filter pruning for efficient neural networks"

Connected through hyperedge HE-5 (induced by source paper "ordering chaos memory aware scheduling of irregularly wired neural networks for edge devices"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "automatic model selection for neural networks"
    - "swiftnet using graph propagation as meta knowledge to search highly representative neural architectures"
    - "discovering neural wirings"
    - "pytorch an imperative style high performance deep learning library"
    - "learned step size quantization"
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-6 (induced by source paper "hrank filter pruning using high rank feature map"):
  with members:
    - "filter sketch for network pruning"
    - "channel pruning via automatic structure search"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "revisiting the importance of individual units in cnns via ablation"
    - "recent advances in efficient computation of deep convolutional neural networks"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"

Connected through hyperedge HE-7 (induced by source paper "pre defined sparsity for low complexity convolutional neural networks"):
  with members:
    - "pre defined sparse neural networks with hardware acceleration"
    - "scienet deep learning with spike assisted contextual information extraction"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-8 (induced by source paper "campfire compressible regularization free structured sparse training for hardware accelerators"):
  with members:
    - "low memory neural network training a technical report"
    - "prunetrain gradual structured pruning from scratch for faster neural network training"
    - "full deep neural network training on a pruned weight budget"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "the state of sparsity in deep neural networks"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "network compression using correlation analysis of layer responses"

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)