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: "sparse networks from scratch faster training without losing performance"
  Abstract: We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving performance levels competitive with dense networks. We accomplish this by developing sparse momentum, an algorithm which uses exponentially smoothed gradients (momentum) to identify layers and weights which reduce the error efficiently. Sparse momentum redistributes pruned weights across layers according to the mean momentum magnitude of each layer. Within a layer, sparse momentum grows weights according to the momentum magnitude of zero-valued weights. We demonstrate state-of-the-art sparse performance on MNIST, CIFAR-10, and ImageNet, decreasing the mean error by a relative 8%, 15%, and 6% compared to other sparse algorithms. Furthermore, we show that our algorithm can reliably find the equivalent of winning lottery tickets from random initialization: Our algorithm finds sparse configurations with 20% or fewer weights which perform as well, or better than their dense counterparts. Sparse momentum also decreases the training time: It requires a single training run -- no re-training is required -- and increases training speed up to 11.85x. In our analysis, we show that our sparse networks might be able to reach dense performance levels by learning more general features which are useful to a broader range of classes than dense networks.

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"
    - "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"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-2 (induced by source paper "picking winning tickets before training by preserving gradient flow"):
  with members:
    - "pre defined sparse neural networks with hardware acceleration"
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a signal propagation perspective for pruning neural networks at initialization"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "the difficulty of training sparse 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"

Connected through hyperedge HE-3 (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"
    - "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"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-4 (induced by source paper "green ai"):
  with members:
    - "defending against neural fake news"
    - "show your work improved reporting of experimental results"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-5 (induced by source paper "cat compression aware training for bandwidth reduction"):
  with members:
    - "ebpc extended bit plane compression for deep neural network inference and training accelerators"
    - "compressibility loss for neural network weights"
    - "feature map transform coding for energy efficient cnn inference"
    - "quantization networks"
    - "lutnet rethinking inference in fpga soft logic"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "efficient and effective quantization for sparse dnns"
    - "focused quantization for sparse cnns"

Connected through hyperedge HE-6 (induced by source paper "the generalization stability tradeoff in neural network pruning"):
  with members:
    - "generalization in deep networks the role of distance from initialization"
    - "gate decorator global filter pruning method for accelerating deep convolutional neural networks"
    - "the state of sparsity in deep neural networks"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-7 (induced by source paper "compressing bert studying the effects of weight pruning on transfer learning"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "low memory neural network training a technical report"
    - "specializing word embeddings for parsing by information bottleneck"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "kermit generative insertion based modeling for sequences"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "using pre training can improve model robustness and uncertainty"

Connected through hyperedge HE-8 (induced by source paper "discovering neural wirings"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "learning sparse networks using targeted dropout"
    - "luck matters understanding training dynamics of deep relu networks"
    - "learning implicitly recurrent cnns through parameter sharing"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "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.LG(Machine Learning)