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: "exploring sparsity in recurrent neural networks"
  Abstract: Recurrent Neural Networks (RNN) are widely used to solve a variety of problems and as the quantity of data and the amount of available compute have increased, so have model sizes. The number of parameters in recent state-of-the-art networks makes them hard to deploy, especially on mobile phones and embedded devices. The challenge is due to both the size of the model and the time it takes to evaluate it. In order to deploy these RNNs efficiently, we propose a technique to reduce the parameters of a network by pruning weights during the initial training of the network. At the end of training, the parameters of the network are sparse while accuracy is still close to the original dense neural network. The network size is reduced by 8x and the time required to train the model remains constant. Additionally, we can prune a larger dense network to achieve better than baseline performance while still reducing the total number of parameters significantly. Pruning RNNs reduces the size of the model and can also help achieve significant inference time speed-up using sparse matrix multiply. Benchmarks show that using our technique model size can be reduced by 90% and speed-up is around 2x to 7x.

Connected through hyperedge HE-1 (induced by source paper "soft threshold weight reparameterization for learnable sparsity"):
  with members:
    - "rigging the lottery making all tickets winners"
    - "sparse networks from scratch faster training without losing performance"
    - "discovering neural wirings"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "fast sparse convnets"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "butterfly transform an efficient fft based neural architecture design"
    - "green ai"

Connected through hyperedge HE-2 (induced by source paper "training simplification and model simplification for deep learning a minimal effort back propagation method"):
  with members:
    - "bag of words as target for neural machine translation"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "neural system combination for machine translation"
    - "attention is all you need"
    - "nonparametric neural networks"
    - "sparse communication for distributed gradient descent"
    - "meprop sparsified back propagation for accelerated deep learning with reduced overfitting"
    - "deep neural machine translation with linear associative unit"

Connected through hyperedge HE-3 (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"

Connected through hyperedge HE-4 (induced by source paper "looking glamorous vehicle re id in heterogeneous cameras networks with global and local attention"):
  with members:
    - "impact of fully connected layers on performance of convolutional neural networks for image classification"
    - "part guided attention learning for vehicle re identification"
    - "attributes guided feature learning for vehicle re identification"
    - "cross domain knowledge learning with dual branch adversarial network for vehicle re identification"
    - "the state of sparsity in deep neural networks"
    - "group normalization"
    - "ram a region aware deep model for vehicle re identification"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-5 (induced by source paper "block wise dynamic sparseness"):
  with members:
    - "block sparse recurrent neural networks"
    - "you look twice gaternet for dynamic filter selection in cnns"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "learning efficient convolutional networks through network slimming"
    - "learning structured sparsity in deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "language modeling with gated convolutional networks"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-6 (induced by source paper "small accurate and fast vehicle re id on the edge the safr approach"):
  with members:
    - "impact of fully connected layers on performance of convolutional neural networks for image classification"
    - "the state of sparsity in deep neural networks"
    - "ram a region aware deep model for vehicle re identification"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "vehicle re identification using quadruple directional deep learning features"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a survey of model compression and acceleration for deep neural networks"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-7 (induced by source paper "recurrent neural networks an embedded computing perspective"):
  with members:
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "single stream parallelization of recurrent neural networks for low power and fast inference"
    - "effective quantization approaches for recurrent neural networks"
    - "alternating multi bit quantization for recurrent neural networks"
    - "a survey on methods and theories of quantized neural networks"
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "tensor decomposition for compressing recurrent neural network"

Connected through hyperedge HE-8 (induced by source paper "ternary mobilenets via per layer hybrid filter banks"):
  with members:
    - "ternary hybrid neural tree networks for highly constrained iot applications"
    - "compressing rnns for iot devices by 15 38x using kronecker products"
    - "searching for mobilenetv3"
    - "run time efficient rnn compression for inference on edge devices"
    - "pushing the limits of rnn compression"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "amc automl for model compression and acceleration on mobile devices"

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)