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: "pruning deep neural networks using partial least squares"
  Abstract: To handle the high computational cost in deep convolutional networks, recent approaches have proposed to find and remove unimportant filters in these networks. Despite achieving remarkable results, these approaches demand a high computational cost mostly because the pruning is performed layer-by-layer, which requires many fine-tuning iterations. In this work, we propose a novel approach to efficiently remove filters in deep convolutional neural networks based on Partial Least Squares (PLS) and Variable Importance in Projection (VIP) to measure the importance of each filter, removing the unimportant (or least important) ones. These techniques allow us to estimate the filter importance based on its contribution in predicting the class label, which we show to be an adequate indicator to remove filters. We validate the proposed method on ImageNet, CIFAR-10 and Food-101 datasets, where it eliminates up to 65% of the filters and reduces 88% of the floating point operations (FLOPs) without penalizing the network accuracy. Additionally, sometimes the method is even able to improve the accuracy compared to the network without pruning. We show that employing PLS+VIP as the criterion for detecting the filters to be removed is better than recent feature selection techniques that have been employed by state-of-the-art pruning methods. Finally, we show that the proposed method is more efficient and achieves a higher reduction in FLOPs than existing methods.

Connected through hyperedge HE-1 (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"
    - "understanding individual neuron importance using information theory"
    - "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-2 (induced by source paper "covariance free partial least squares an incremental dimensionality reduction method"):
  with members:
    - "learning multiple visual domains with residual adapters"
    - "infinite latent feature selection a probabilistic latent graph based ranking approach"
    - "unconstrained still video based face verification with deep convolutional neural networks"
    - "fast r cnn"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"

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.CV(Computer Vision and Pattern Recognition)