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: "eigendamage structured pruning in the kronecker factored eigenbasis"
  Abstract: Reducing the test time resource requirements of a neural network while preserving test accuracy is crucial for running inference on resource-constrained devices. To achieve this goal, we introduce a novel network reparameterization based on the Kronecker-factored eigenbasis (KFE), and then apply Hessian-based structured pruning methods in this basis. As opposed to existing Hessian-based pruning algorithms which do pruning in parameter coordinates, our method works in the KFE where different weights are approximately independent, enabling accurate pruning and fast computation. We demonstrate empirically the effectiveness of the proposed method through extensive experiments. In particular, we highlight that the improvements are especially significant for more challenging datasets and networks. With negligible loss of accuracy, an iterative-pruning version gives a 10$\times$ reduction in model size and a 8$\times$ reduction in FLOPs on wide ResNet32.

Connected through hyperedge HE-1 (induced by source paper "picking winning tickets before training by preserving gradient flow"):
  with members:
    - "pre defined sparse neural networks with hardware acceleration"
    - "sparse networks from scratch faster training without losing performance"
    - "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-2 (induced by source paper "dissecting non vacuous generalization bounds based on the mean field approximation"):
  with members:
    - "in defense of uniform convergence generalization via derandomization with an application to interpolating predictors"
    - "compression based bound for non compressed network unified generalization error analysis of large compressible deep neural network"
    - "hessian based analysis of sgd for deep nets dynamics and generalization"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "limitations of the empirical fisher approximation"
    - "information theoretic local minima characterization and regularization"
    - "the information complexity of learning tasks their structure and their distance"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"

Connected through hyperedge HE-3 (induced by source paper "identifying critical neurons in ann architectures using mixed integer programming"):
  with members:
    - "lossless compression of deep neural networks"
    - "pruning at a glance global neural pruning for model compression"
    - "a benchmark for interpretability methods in deep neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "understanding individual neuron importance using information theory"
    - "pruning deep neural networks using partial least squares"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-4 (induced by source paper "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"):
  with members:
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "understanding short horizon bias in stochastic meta optimization"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "an empirical model of large batch training"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "eigenvalue corrected noisy natural gradient"
    - "three mechanisms of weight decay regularization"

Connected through hyperedge HE-5 (induced by source paper "taxonomy and evaluation of structured compression of convolutional neural networks"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "the state of sparsity in deep neural networks"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "efficient neural network compression"

Connected through hyperedge HE-6 (induced by source paper "better pac bayes bounds for deep neural networks using the loss curvature"):
  with members:
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "limitations of the empirical fisher approximation"
    - "information matrices and generalization"
    - "the information complexity of learning tasks their structure and their distance"
    - "some limitations of norm based generalization bounds in deep neural networks"
    - "data dependent pac bayes priors via differential privacy"
    - "fixing variational bayes deterministic variational inference for bayesian neural networks"
    - "fisher rao metric geometry and complexity of 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.LG(Machine Learning)