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: "deconstructing lottery tickets zeros signs and the supermask"
  Abstract: The recent "Lottery Ticket Hypothesis" paper by Frankle & Carbin showed that a simple approach to creating sparse networks (keeping the large weights) results in models that are trainable from scratch, but only when starting from the same initial weights. The performance of these networks often exceeds the performance of the non-sparse base model, but for reasons that were not well understood. In this paper we study the three critical components of the Lottery Ticket (LT) algorithm, showing that each may be varied significantly without impacting the overall results. Ablating these factors leads to new insights for why LT networks perform as well as they do. We show why setting weights to zero is important, how signs are all you need to make the reinitialized network train, and why masking behaves like training. Finally, we discover the existence of Supermasks, masks that can be applied to an untrained, randomly initialized network to produce a model with performance far better than chance (86% on MNIST, 41% on CIFAR-10).

Connected through hyperedge HE-1 (induced by source paper "learned threshold pruning"):
  with members:
    - "taxonomy and evaluation of structured compression of convolutional neural networks"
    - "mixconv mixed depthwise convolutional kernels"
    - "progressive dnn compression a key to achieve ultra high weight pruning and quantization rates using admm"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "ai benchmark running deep neural networks on android smartphones"

Connected through hyperedge HE-2 (induced by source paper "pruning convolutional neural networks with self supervision"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "linear mode connectivity and the lottery ticket hypothesis"
    - "contrastive multiview coding"
    - "unsupervised pre training of image features on non curated data"
    - "data efficient image recognition with contrastive predictive coding"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "learning representations by maximizing mutual information across views"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-3 (induced by source paper "on iterative neural network pruning reinitialization and the similarity of masks"):
  with members:
    - "on activation function coresets for network pruning"
    - "exploring randomly wired neural networks for image recognition"
    - "the state of sparsity in deep neural networks"
    - "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 "proving the lottery ticket hypothesis pruning is all you need"):
  with members:
    - "effect of activation functions on the training of overparametrized neural nets"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "what s hidden in a randomly weighted neural network"
    - "on the power and limitations of random features for understanding neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "complexity of training relu neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "the early phase of neural network training"):
  with members:
    - "stabilizing the lottery ticket hypothesis"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "unsupervised representation learning by predicting image rotations"
    - "gradient descent happens in a tiny subspace"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-6 (induced by source paper "training batchnorm and only batchnorm on the expressive power of random features in cnns"):
  with members:
    - "a mean field theory of batch normalization"
    - "what s hidden in a randomly weighted neural network"
    - "on the power and limitations of random features for understanding neural networks"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "towards understanding regularization in batch normalization"
    - "understanding batch normalization"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "the shattered gradients problem if resnets are the answer then what is the question"

Connected through hyperedge HE-7 (induced by source paper "permute to train a new dimension to training deep neural networks"):
  with members:
    - "weight agnostic neural networks"
    - "what s hidden in a randomly weighted neural network"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "neural networks are surprisingly modular"):
  with members:
    - "linear mode connectivity and the lottery ticket hypothesis"
    - "nif a framework for quantifying neural information flow in deep networks"
    - "what s hidden in a randomly weighted neural network"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "learning both weights and connections for efficient neural networks"
    - "adam a method for stochastic optimization"
    - "a tutorial on spectral clustering"

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)