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: "what s hidden in a randomly weighted neural network"
  Abstract: Training a neural network is synonymous with learning the values of the weights. In contrast, we demonstrate that randomly weighted neural networks contain subnetworks which achieve impressive performance without ever training the weight values. Hidden in a randomly weighted Wide ResNet-50 we show that there is a subnetwork (with random weights) that is smaller than, but matches the performance of a ResNet-34 trained on ImageNet. Not only do these "untrained subnetworks" exist, but we provide an algorithm to effectively find them. We empirically show that as randomly weighted neural networks with fixed weights grow wider and deeper, an "untrained subnetwork" approaches a network with learned weights in accuracy.

Connected through hyperedge HE-1 (induced by source paper "good subnetworks provably exist pruning via greedy forward selection"):
  with members:
    - "proving the lottery ticket hypothesis pruning is all you need"
    - "pruning from scratch"
    - "sipping neural networks sensitivity informed provable pruning of neural networks"
    - "searching for mobilenetv3"
    - "data independent neural pruning via coresets"
    - "legr filter pruning via learned global ranking"
    - "provable filter pruning for efficient neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "on pruning adversarially robust neural networks"):
  with members:
    - "picking winning tickets before training by preserving gradient flow"
    - "certified adversarial robustness via randomized smoothing"
    - "intriguing properties of adversarial training"
    - "robust sparse regularization simultaneously optimizing neural network robustness and compactness"
    - "towards compact and robust deep neural networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "towards stable and efficient training of verifiably robust neural networks"

Connected through hyperedge HE-3 (induced by source paper "proving the lottery ticket hypothesis pruning is all you need"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "effect of activation functions on the training of overparametrized neural nets"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "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-4 (induced by source paper "training batchnorm and only batchnorm on the expressive power of random features in cnns"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "a mean field theory of batch normalization"
    - "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-5 (induced by source paper "deep randomized neural networks"):
  with members:
    - "richness of deep echo state network dynamics"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "comparison between deepesns and gated rnns on multivariate time series prediction"
    - "deep semi random features for nonlinear function approximation"
    - "stochastic configuration networks fundamentals and algorithms"
    - "multiplex visibility graphs to investigate recurrent neural network dynamics"
    - "deep stochastic configuration networks with universal approximation property"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"

Connected through hyperedge HE-6 (induced by source paper "permute to train a new dimension to training deep neural networks"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "weight agnostic neural networks"
    - "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-7 (induced by source paper "neural networks are surprisingly modular"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "linear mode connectivity and the lottery ticket hypothesis"
    - "nif a framework for quantifying neural information flow in deep networks"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "linear mode connectivity and the lottery ticket hypothesis"):
  with members:
    - "the early phase of neural network training"
    - "picking winning tickets before training by preserving gradient flow"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "an exponential learning rate schedule for deep learning"
    - "the state of sparsity in deep neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

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)