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: "a note on randomized element wise matrix sparsification"
  Abstract: Given a matrix A \in R^{m x n}, we present a randomized algorithm that sparsifies A by retaining some of its elements by sampling them according to a distribution that depends on both the square and the absolute value of the entries. We combine the ideas of [4, 1] and provide an elementary proof of the approximation accuracy of our algorithm following [4] without the truncation step.

Connected through hyperedge HE-1 (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-2 (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"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "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"

Connected through hyperedge HE-3 (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"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "data dependent coresets for compressing neural networks with applications to generalization bounds"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "spectrally normalized margin bounds for neural networks"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "compression aware training of deep networks"

Connected through hyperedge HE-5 (induced by source paper "exploiting numerical sparsity for efficient learning faster eigenvector computation and regression"):
  with members:
    - "spectrum approximation beyond fast matrix multiplication algorithms and hardness"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "near optimal entrywise sampling for data matrices"

Connected through hyperedge HE-6 (induced by source paper "recovering pca from hybrid l 1 l 2 sparse sampling of data elements"):
  with members:
    - "a note on element wise matrix sparsification via a matrix valued bernstein inequality"
    - "a simpler approach to matrix completion"

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.IT(Information Theory)