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: "play and prune adaptive filter pruning for deep model compression"
  Abstract: While convolutional neural networks (CNN) have achieved impressive performance on various classification/recognition tasks, they typically consist of a massive number of parameters. This results in significant memory requirement as well as computational overheads. Consequently, there is a growing need for filter-level pruning approaches for compressing CNN based models that not only reduce the total number of parameters but reduce the overall computation as well. We present a new min-max framework for filter-level pruning of CNNs. Our framework, called Play and Prune (PP), jointly prunes and fine-tunes CNN model parameters, with an adaptive pruning rate, while maintaining the model's predictive performance. Our framework consists of two modules: (1) An adaptive filter pruning (AFP) module, which minimizes the number of filters in the model; and (2) A pruning rate controller (PRC) module, which maximizes the accuracy during pruning. Moreover, unlike most previous approaches, our approach allows directly specifying the desired error tolerance instead of pruning level. Our compressed models can be deployed at run-time, without requiring any special libraries or hardware. Our approach reduces the number of parameters of VGG-16 by an impressive factor of 17.5X, and number of FLOPS by 6.43X, with no loss of accuracy, significantly outperforming other state-of-the-art filter pruning methods.

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"
    - "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"
    - "probabilistic model agnostic meta learning"

Connected through hyperedge HE-2 (induced by source paper "channel pruning via automatic structure search"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "cop customized deep model compression via regularized correlation based filter level pruning"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "haq hardware aware automated quantization with mixed precision"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "filter sketch for network pruning"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "bilinear attention networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "haq hardware aware automated quantization with mixed precision"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "data driven sparse structure selection for deep neural networks"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-4 (induced by source paper "cooperative initialization based deep neural network training"):
  with members:
    - "accuracy booster performance boosting using feature map re calibration"
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "cpwc contextual point wise convolution for object recognition"
    - "stability based filter pruning for accelerating deep cnns"
    - "multi layer pruning framework for compressing single shot multibox detector"
    - "leveraging filter correlations for deep model compression"
    - "parametric exponential linear unit for deep convolutional neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "localization aware channel pruning for object detection"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "focused quantization for sparse cnns"
    - "mask scoring r cnn"
    - "objects as points"
    - "cornernet detecting objects as paired keypoints"
    - "efficient neural network compression"
    - "discrimination aware channel pruning for deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "a survey of pruning methods for efficient person re identification across domains"):
  with members:
    - "building efficient convnets using redundant feature pruning"
    - "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-7 (induced by source paper "accuracy booster performance boosting using feature map re calibration"):
  with members:
    - "cooperative initialization based deep neural network training"
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "cpwc contextual point wise convolution for object recognition"
    - "stability based filter pruning for accelerating deep cnns"
    - "multi layer pruning framework for compressing single shot multibox detector"
    - "leveraging filter correlations for deep model compression"
    - "gather excite exploiting feature context in convolutional neural networks"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-8 (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"
    - "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"
    - "bayesian model agnostic meta learning"

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)