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: "understanding individual neuron importance using information theory"
  Abstract: In this work, we characterize the outputs of individual neurons in a trained feed-forward neural network by entropy, mutual information with the class variable, and a class selectivity measure based on Kullback-Leibler divergence. By cumulatively ablating neurons in the network, we connect these information-theoretic measures to the impact their removal has on classification performance on the test set. We observe that, looking at the neural network as a whole, none of these measures is a good indicator for classification performance, thus confirming recent results by Morcos et al. However, looking at specific layers separately, both mutual information and class selectivity are positively correlated with classification performance. We thus conclude that it is ill-advised to compare these measures across layers, and that different layers may be most appropriately characterized by different measures. #R##N#We then discuss pruning neurons from neural networks to reduce computational complexity of inference. Drawing from our results, we perform pruning based on information-theoretic measures on a fully connected feed-forward neural network with two hidden layers trained on MNIST dataset and compare the results to a recently proposed pruning method. We furthermore show that the common practice of re-training after pruning can partly be obviated by a surgery step called bias balancing, without incurring significant performance degradation.

Connected through hyperedge HE-1 (induced by source paper "on iterative neural network pruning reinitialization and the similarity of masks"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "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"
    - "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-2 (induced by source paper "identifying critical neurons in ann architectures using mixed integer programming"):
  with members:
    - "lossless compression of deep neural networks"
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "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"
    - "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-3 (induced by source paper "a novel approach to multivariate redundancy and synergy"):
  with members:
    - "unique information and secret key agreement"
    - "comparison of channels criteria for domination by a symmetric channel"
    - "pointwise partial information decomposition using the specificity and ambiguity lattices"
    - "secret sharing and shared information"
    - "measuring multivariate redundant information with pointwise common change in surprisal"
    - "quantifying synergistic information using intermediate stochastic variables"
    - "on the input degradedness and input equivalence between channels"
    - "synergy redundancy and common information"

Connected through hyperedge HE-4 (induced by source paper "learning representations for neural network based classification using the information bottleneck principle"):
  with members:
    - "uncertainty in the variational information bottleneck"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "i revnet deep invertible networks"
    - "estimating information flow in neural networks"
    - "the variational deficiency bottleneck"
    - "emergence of invariance and disentanglement in deep representations"
    - "opening the black box of deep neural networks via information"
    - "neural networks regularization through class wise invariant representation learning"

Connected through hyperedge HE-5 (induced by source paper "understanding convolutional neural networks with information theory an initial exploration"):
  with members:
    - "adaptive estimators show information compression in deep neural networks"
    - "understanding autoencoders with information theoretic concepts"
    - "estimating information flow in neural networks"
    - "simple stopping criteria for information theoretic feature selection"
    - "emergence of invariance and disentanglement in deep representations"
    - "thinet a filter level pruning method for deep neural network compression"
    - "opening the black box of deep neural networks via information"
    - "the hasyv2 dataset"

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)