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: "faster cnns with direct sparse convolutions and guided pruning"
  Abstract: Phenomenally successful in practical inference problems, convolutional neural networks (CNN) are widely deployed in mobile devices, data centers, and even supercomputers. The number of parameters needed in CNNs, however, are often large and undesirable. Consequently, various methods have been developed to prune a CNN once it is trained. Nevertheless, the resulting CNNs offer limited benefits. While pruning the fully connected layers reduces a CNN's size considerably, it does not improve inference speed noticeably as the compute heavy parts lie in convolutions. Pruning CNNs in a way that increase inference speed often imposes specific sparsity structures, thus limiting the achievable sparsity levels. #R##N#We present a method to realize simultaneously size economy and speed improvement while pruning CNNs. Paramount to our success is an efficient general sparse-with-dense matrix multiplication implementation that is applicable to convolution of feature maps with kernels of arbitrary sparsity patterns. Complementing this, we developed a performance model that predicts sweet spots of sparsity levels for different layers and on different computer architectures. Together, these two allow us to demonstrate 3.1--7.3$\times$ convolution speedups over dense convolution in AlexNet, on Intel Atom, Xeon, and Xeon Phi processors, spanning the spectrum from mobile devices to supercomputers. We also open source our project at this https URL.

Connected through hyperedge HE-1 (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-2 (induced by source paper "comparing rewinding and fine tuning in neural network pruning"):
  with members:
    - "what is the state of neural network pruning"
    - "linear mode connectivity and the lottery ticket hypothesis"
    - "sparse networks from scratch faster training without losing performance"
    - "a constructive prediction of the generalization error across scales"
    - "mlperf training benchmark"
    - "the state of sparsity in deep neural networks"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-3 (induced by source paper "neural network compression framework for fast model inference"):
  with members:
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "learning sparse networks using targeted dropout"
    - "the state of sparsity in deep neural networks"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "pact parameterized clipping activation for quantized neural networks"
    - "mnasnet platform aware neural architecture search for mobile"
    - "progressive neural architecture search"

Connected through hyperedge HE-4 (induced by source paper "the two pass softmax algorithm"):
  with members:
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"
    - "building a web scale dependency parsed corpus from commoncrawl"
    - "high performance ultra low precision convolutions on mobile devices"
    - "efficient softmax approximation for gpus"
    - "eie efficient inference engine on compressed deep neural network"
    - "exploring the limits of language modeling"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "automatic generation of efficient sparse tensor format conversion routines"):
  with members:
    - "format abstraction for sparse tensor algebracompilers"
    - "a unified optimization approach for sparse tensor operations on gpus"
    - "csr5 an efficient storage format for cross platform sparse matrix vector multiplication"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-6 (induced by source paper "low memory neural network training a technical report"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "pay less attention with lightweight and dynamic convolutions"
    - "learning fast algorithms for linear transforms using butterfly factorizations"
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "memory efficient adaptive optimization"
    - "sparse evolutionary deep learning with over one million artificial neurons on commodity hardware"
    - "fixup initialization residual learning without normalization"
    - "full deep neural network training on a pruned weight budget"

Connected through hyperedge HE-7 (induced by source paper "towards optimal structured cnn pruning via generative adversarial learning"):
  with members:
    - "towards compact convnets via structure sparsity regularized filter pruning"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "discrimination aware channel pruning for deep neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"

Connected through hyperedge HE-8 (induced by source paper "asymptotic soft filter pruning for deep convolutional neural networks"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "shakeout a new approach to regularized deep neural network training"
    - "learning to prune filters in convolutional neural networks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "discrimination aware channel pruning for deep neural networks"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "coreset based neural network compression"
    - "learning efficient convolutional networks through network slimming"

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)