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: "data dependent coresets for compressing neural networks with applications to generalization bounds"
  Abstract: We present an efficient coresets-based neural network compression algorithm that sparsifies the parameters of a trained fully-connected neural network in a manner that provably approximates the network's output. Our approach is based on an importance sampling scheme that judiciously defines a sampling distribution over the neural network parameters, and as a result, retains parameters of high importance while discarding redundant ones. We leverage a novel, empirical notion of sensitivity and extend traditional coreset constructions to the application of compressing parameters. Our theoretical analysis establishes guarantees on the size and accuracy of the resulting compressed network and gives rise to generalization bounds that may provide new insights into the generalization properties of neural networks. We demonstrate the practical effectiveness of our algorithm on a variety of neural network configurations and real-world data sets.

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"
    - "what s hidden in a randomly weighted neural network"
    - "provable filter pruning for efficient neural networks"

Connected through hyperedge HE-2 (induced by source paper "on coresets for support vector machines"):
  with members:
    - "provable filter pruning for efficient neural networks"
    - "new frameworks for offline and streaming coreset constructions"
    - "accurate streaming support vector machines"
    - "fast svm training using approximate extreme points"
    - "a unified framework for approximating and clustering data"
    - "sublinear optimization for machine learning"
    - "streamed learning one pass svms"

Connected through hyperedge HE-3 (induced by source paper "provable filter pruning for efficient neural networks"):
  with members:
    - "revisiting hard thresholding for dnn pruning"
    - "sipping neural networks sensitivity informed provable pruning of neural networks"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "learning filter basis for convolutional neural network compression"
    - "the state of sparsity in deep neural networks"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "learning to prune filters in convolutional neural networks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"

Connected through hyperedge HE-4 (induced by source paper "compression based bound for non compressed network unified generalization error analysis of large compressible deep neural network"):
  with members:
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "sipping neural networks sensitivity informed provable pruning of neural networks"):
  with members:
    - "revisiting hard thresholding for dnn pruning"
    - "on coresets for logistic regression"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "snip single shot network pruning based on connection sensitivity"
    - "rethinking the value of network pruning"
    - "training support vector machines using coresets"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-6 (induced by source paper "data independent neural pruning via coresets"):
  with members:
    - "the state of sparsity in deep neural networks"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "discrimination aware channel pruning for deep neural networks"
    - "a convergence theory for deep learning via over parameterization"
    - "coreset based neural network compression"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "compression aware training of deep networks"

Connected through hyperedge HE-7 (induced by source paper "revisiting hard thresholding for dnn pruning"):
  with members:
    - "fast convex pruning of deep neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "compressing neural networks using the variational information bottleneck"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "spectrally normalized margin bounds for neural networks"
    - "size independent sample complexity of neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"

Connected through hyperedge HE-8 (induced by source paper "learning overparameterized neural networks via stochastic gradient descent on structured data"):
  with members:
    - "generalization in deep networks the role of distance from initialization"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "theoretical properties of the global optimizer of two layer neural network"
    - "learning one hidden layer neural networks with landscape design"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "recovery guarantees for one hidden layer neural networks"
    - "theoretical insights into the optimization landscape of over parameterized shallow 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)