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: "structural compression of convolutional neural networks based on greedy filter pruning"
  Abstract: Convolutional neural networks (CNNs) have state-of-the-art performance on many problems in machine vision. However, networks with superior performance often have millions of weights so that it is difficult or impossible to use CNNs on computationally limited devices or to humanly interpret them. A myriad of CNN compression approaches have been proposed and they involve pruning and compressing the weights and filters. In this article, we introduce a greedy structural compression scheme that prunes filters in a trained CNN. We define a filter importance index equal to the classification accuracy reduction (CAR) of the network after pruning that filter (similarly defined as RAR for regression). We then iteratively prune filters based on the CAR index. This algorithm achieves substantially higher classification accuracy in AlexNet compared to other structural compression schemes that prune filters. Pruning half of the filters in the first or second layer of AlexNet, our CAR algorithm achieves 26% and 20% higher classification accuracies respectively, compared to the best benchmark filter pruning scheme. Our CAR algorithm, combined with further weight pruning and compressing, reduces the size of first or second convolutional layer in AlexNet by a factor of 42, while achieving close to original classification accuracy through retraining (or fine-tuning) network. Finally, we demonstrate the interpretability of CAR-compressed CNNs by showing that our algorithm prunes filters with visually redundant functionalities. In fact, out of top 20 CAR-pruned filters in AlexNet, 17 of them in the first layer and 14 of them in the second layer are color-selective filters as opposed to shape-selective filters. To our knowledge, this is the first reported result on the connection between compression and interpretability of CNNs.

Connected through hyperedge HE-1 (induced by source paper "a network pruning network approach to deep model compression"):
  with members:
    - "cooperative initialization based deep neural network training"
    - "accuracy booster performance boosting using feature map re calibration"
    - "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"
    - "multi layer pruning framework for compressing single shot multibox detector"
    - "amc automl for model compression and acceleration on mobile devices"
    - "leveraging filter correlations for deep model compression"

Connected through hyperedge HE-2 (induced by source paper "centripetal sgd for pruning very deep convolutional networks with complicated structure"):
  with members:
    - "towards compact convnets via structure sparsity regularized filter pruning"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "leveraging filter correlations for deep model compression"
    - "discrimination aware channel pruning for deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "structured pruning for efficient convnets via incremental regularization"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-3 (induced by source paper "parsimonious deep learning a differential inclusion approach with global convergence"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "gradient descent finds global minima of deep neural networks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "msplit lbi realizing feature selection and dense estimation simultaneously in few shot and zero shot learning"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "a convergence theory for deep learning via over parameterization"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "neural network distiller a python package for dnn compression research"):
  with members:
    - "fakta an automatic end to end fact checking system"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "sinreq generalized sinusoidal regularization for low bitwidth deep quantized training"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "attention based guided structured sparsity of deep neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "post training 4 bit quantization of convolution networks for rapid deployment"

Connected through hyperedge HE-5 (induced by source paper "approximated oracle filter pruning for destructive cnn width optimization"):
  with members:
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "learning to prune filters in convolutional neural networks"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "the incredible shrinking neural network new perspectives on learning representations through the lens of pruning"
    - "structured probabilistic pruning for convolutional neural network acceleration"

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"

Connected through hyperedge HE-7 (induced by source paper "stability based filter pruning for accelerating deep cnns"):
  with members:
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "learning efficient convolutional networks through network slimming"
    - "thinet a filter level pruning method for deep neural network compression"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "an entropy based pruning method for cnn compression"
    - "learning structured sparsity in deep neural networks"
    - "pruning convolutional neural networks for resource efficient inference"

Connected through hyperedge HE-8 (induced by source paper "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"):
  with members:
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "the power of sparsity in convolutional neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "ultimate tensorization compressing convolutional and fc layers alike"
    - "wide residual networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

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)