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: "learning to prune filters in convolutional neural networks"
  Abstract: Many state-of-the-art computer vision algorithms use large scale convolutional neural networks (CNNs) as basic building blocks. These CNNs are known for their huge number of parameters, high redundancy in weights, and tremendous computing resource consumptions. This paper presents a learning algorithm to simplify and speed up these CNNs. Specifically, we introduce a "try-and-learn" algorithm to train pruning agents that remove unnecessary CNN filters in a data-driven way. With the help of a novel reward function, our agents removes a significant number of filters in CNNs while maintaining performance at a desired level. Moreover, this method provides an easy control of the tradeoff between network performance and its scale. Per- formance of our algorithm is validated with comprehensive pruning experiments on several popular CNNs for visual recognition and semantic segmentation tasks.

Connected through hyperedge HE-1 (induced by source paper "sasl saliency adaptive sparsity learning for neural network acceleration"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "oicsr out in channel sparsity regularization for compact deep neural networks"
    - "importance estimation for neural network pruning"
    - "global sparse momentum sgd for pruning very deep neural networks"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "structured pruning of neural networks with budget aware regularization"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-2 (induced by source paper "convolutional neural network pruning using filter attenuation"):
  with members:
    - "modeling of pruning techniques for deep neural networks simplification"
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "prune the convolutional neural networks with sparse shrink"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "thinet a filter level pruning method for deep neural network compression"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "pcnn pattern based fine grained regular pruning towards optimizing cnn accelerators"):
  with members:
    - "non structured dnn weight pruning considered harmful"
    - "req yolo a resource aware efficient quantization framework for object detection on fpgas"
    - "snip single shot network pruning based on connection sensitivity"
    - "efficient hardware realization of convolutional neural networks using intra kernel regular pruning"
    - "learning efficient convolutional networks through network slimming"
    - "learning structured sparsity in deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "pruning filters for efficient convnets"

Connected through hyperedge HE-4 (induced by source paper "modeling of pruning techniques for deep neural networks simplification"):
  with members:
    - "asymptotic soft filter pruning for deep convolutional neural networks"
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "discrimination aware channel pruning for deep neural networks"
    - "snip single shot network pruning based on connection sensitivity"
    - "learning efficient convolutional networks through network slimming"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "thinet a filter level pruning method for deep neural network compression"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "retrain or not retrain efficient pruning methods of deep cnn networks"):
  with members:
    - "convolutional neural network compression for natural language processing"
    - "snip single shot network pruning based on connection sensitivity"
    - "improving text classification with vectors of reduced precision"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-6 (induced by source paper "autoslim towards one shot architecture search for channel numbers"):
  with members:
    - "universally slimmable networks and improved training techniques"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "slimmable neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "graph hypernetworks for neural architecture search"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-7 (induced by source paper "provable filter pruning for efficient neural networks"):
  with members:
    - "revisiting hard thresholding for dnn pruning"
    - "sipping neural networks sensitivity informed provable pruning of neural networks"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "learning filter basis for convolutional neural network compression"
    - "the state of sparsity in deep neural networks"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"

Connected through hyperedge HE-8 (induced by source paper "asymptotic soft filter pruning for deep convolutional neural networks"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "shakeout a new approach to regularized deep neural network training"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "discrimination aware channel pruning for deep neural networks"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "coreset based neural network compression"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"

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)