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: "the state of sparsity in deep neural networks"
  Abstract: We rigorously evaluate three state-of-the-art techniques for inducing sparsity in deep neural networks on two large-scale learning tasks: Transformer trained on WMT 2014 English-to-German, and ResNet-50 trained on ImageNet. Across thousands of experiments, we demonstrate that complex techniques (Molchanov et al., 2017; Louizos et al., 2017b) shown to yield high compression rates on smaller datasets perform inconsistently, and that simple magnitude pruning approaches achieve comparable or better results. Additionally, we replicate the experiments performed by (Frankle & Carbin, 2018) and (Liu et al., 2018) at scale and show that unstructured sparse architectures learned through pruning cannot be trained from scratch to the same test set performance as a model trained with joint sparsification and optimization. Together, these results highlight the need for large-scale benchmarks in the field of model compression. We open-source our code, top performing model checkpoints, and results of all hyperparameter configurations to establish rigorous baselines for future work on compression and sparsification.

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 "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"
    - "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"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-3 (induced by source paper "on iterative neural network pruning reinitialization and the similarity of masks"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "on activation function coresets for network pruning"
    - "exploring randomly wired neural networks for image recognition"
    - "how important is a neuron"
    - "understanding individual neuron importance using information theory"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "revisiting the importance of individual units in cnns via ablation"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-4 (induced by source paper "privacy preserving learning via deep net pruning"):
  with members:
    - "deep leakage from gradients"
    - "stronger l2 l2 compressed sensing without iterating"
    - "inverting deep generative models one layer at a time"
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "graph convolutional neural networks for web scale recommender systems"
    - "federated learning strategies for improving communication efficiency"
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"

Connected through hyperedge HE-5 (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"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-6 (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"
    - "group normalization"
    - "ram a region aware deep model for vehicle re identification"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"

Connected through hyperedge HE-7 (induced by source paper "rnnpool efficient non linear pooling for ram constrained inference"):
  with members:
    - "visual wake words dataset"
    - "extd extremely tiny face detector via iterative filter reuse"
    - "lffd a light and fast face detector for edge devices"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-8 (induced by source paper "lookahead a far sighted alternative of magnitude based pruning"):
  with members:
    - "backpack packing more into backprop"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the singular values of convolutional layers"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"

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)