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: "building efficient convnets using redundant feature pruning"
  Abstract: This paper presents an efficient technique to prune deep and/or wide convolutional neural network models by eliminating redundant features (or filters). Previous studies have shown that over-sized deep neural network models tend to produce a lot of redundant features that are either shifted version of one another or are very similar and show little or no variations; thus resulting in filtering redundancy. We propose to prune these redundant features along with their connecting feature maps according to their differentiation and based on their relative cosine distances in the feature space, thus yielding smaller network size with reduced inference costs and competitive performance. We empirically show on select models and CIFAR-10 dataset that inference costs can be reduced by 40% for VGG-16, 27% for ResNet-56, and 39% for ResNet-110.

Connected through hyperedge HE-1 (induced by source paper "on correlation of features extracted by deep neural networks"):
  with members:
    - "diversity regularized adversarial learning"
    - "deep learning of constrained autoencoders for enhanced understanding of data"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "self normalizing neural networks"
    - "the power of sparsity in convolutional neural networks"
    - "learning structured sparsity in deep neural networks"
    - "wavenet a generative model for raw audio"
    - "dsd dense sparse dense training for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "diversity regularized adversarial learning"):
  with members:
    - "the gan landscape losses architectures regularization and normalization"
    - "spectral normalization for generative adversarial networks"
    - "deep learning of constrained autoencoders for enhanced understanding of data"
    - "stabilizing training of generative adversarial networks through regularization"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "multi content gan for few shot font style transfer"
    - "neural photo editing with introspective adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "a survey of pruning methods for efficient person re identification across domains"):
  with members:
    - "play and prune adaptive filter pruning for deep model compression"
    - "progressive deep neural networks acceleration via soft filter pruning"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "end to end comparative attention networks for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "an entropy based pruning method for cnn compression"
    - "channel pruning for accelerating very deep neural networks"
    - "gated siamese convolutional neural network architecture for human re identification"

Connected through hyperedge HE-4 (induced by source paper "efficient architecture for deep neural networks with heterogeneous sensitivity"):
  with members:
    - "deep asymmetric networks with a set of node wise variant activation functions"
    - "discrimination aware channel pruning for deep neural networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "a survey of model compression and acceleration for deep neural networks"
    - "an entropy based pruning method for cnn compression"
    - "channel pruning for accelerating very deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "pruning filters for efficient convnets"

Connected through hyperedge HE-5 (induced by source paper "how compact assessing compactness of representations through layer wise pruning"):
  with members:
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "learning efficient convolutional networks through network slimming"
    - "mask r cnn"
    - "channel pruning for accelerating very deep neural networks"
    - "yolo9000 better faster stronger"
    - "pruning convolutional neural networks for resource efficient inference"
    - "pruning filters for efficient convnets"

Connected through hyperedge HE-6 (induced by source paper "leveraging filter correlations for deep model compression"):
  with members:
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "cpwc contextual point wise convolution for object recognition"
    - "play and prune adaptive filter pruning for deep model compression"
    - "stability based filter pruning for accelerating deep cnns"
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "multi layer pruning framework for compressing single shot multibox detector"
    - "amc automl for model compression and acceleration on mobile devices"
    - "model compression via distillation and quantization"

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)