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: "on activation function coresets for network pruning"
  Abstract: Model compression provides a means to efficiently deploy deep neural networks (DNNs) on devices that limited computation resources and tight power budgets, such as mobile and IoT (Internet of Things) devices. Consequently, model compression is one of the most critical topics in modern deep learning. Typically, the state-of-the-art model compression methods suffer from a big limitation: they are only based on heuristics rather than theoretical foundation and thus offer no worst-case guarantees. To bridge this gap, Baykal et. al. [2018a] suggested using a coreset, a small weighted subset of the data that provably approximates the original data set, to sparsify the parameters of a trained fully-connected neural network by sampling a number of neural network parameters based on the importance of the data. However, the sampling procedure is data-dependent and can only be only be performed after an expensive training phase. #R##N#We propose the use of data-independent coresets to perform provable model compression without the need for training. We first prove that there exists a coreset whose size is independent of the input size of the data for any neuron whose activation function is from a family of functions that includes variants of ReLU, sigmoid and others. We then provide a compression-based algorithm that constructs these coresets and explicitly applies neuron pruning for the underlying model. We demonstrate the effectiveness of our methods with experimental evaluations for both synthetic and real-world benchmark network compression. In particular, our framework provides up to 90% compression on the LeNet-300-100 architecture on MNIST and actually improves the accuracy.

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"
    - "exploring randomly wired neural networks for image recognition"
    - "the state of sparsity in deep neural networks"
    - "how important is a neuron"
    - "understanding individual neuron importance using information theory"
    - "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"

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)