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: "metapruning meta learning for automatic neural network channel pruning"
  Abstract: In this paper, we propose a novel meta learning approach for automatic channel pruning of very deep neural networks. We first train a PruningNet, a kind of meta network, which is able to generate weight parameters for any pruned structure given the target network. We use a simple stochastic structure sampling method for training the PruningNet. Then, we apply an evolutionary procedure to search for good-performing pruned networks. The search is highly efficient because the weights are directly generated by the trained PruningNet and we do not need any finetuning. With a single PruningNet trained for the target network, we can search for various Pruned Networks under different constraints with little human participation. We have demonstrated competitive performances on MobileNet V1/V2 networks, up to 9.0/9.9 higher ImageNet accuracy than V1/V2. Compared to the previous state-of-the-art AutoML-based pruning methods, like AMC and NetAdapt, we achieve higher or comparable accuracy under various conditions.

Connected through hyperedge HE-1 (induced by source paper "localizing multi scale semantic patches for image classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "saccader improving accuracy of hard attention models for vision"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "searching for mobilenetv3"
    - "gated convolutional networks with hybrid connectivity for image classification"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "cbam convolutional block attention module"
    - "smoothgrad removing noise by adding noise"

Connected through hyperedge HE-2 (induced by source paper "domain aware dynamic networks"):
  with members:
    - "test time training for out of distribution generalization"
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "multi source domain adaptation for semantic segmentation"
    - "condconv conditionally parameterized convolutions for efficient inference"
    - "searching for mobilenetv3"
    - "single path one shot neural architecture search with uniform sampling"
    - "tafe net task aware feature embeddings for low shot learning"

Connected through hyperedge HE-3 (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"
    - "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"
    - "learning to prune filters in convolutional neural networks"
    - "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-4 (induced by source paper "towards learning of filter level heterogeneous compression of convolutional neural networks"):
  with members:
    - "improving neural architecture search image classifiers via ensemble learning"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "regularizing activation distribution for training binarized deep networks"
    - "multi objective reinforced evolution in mobile neural architecture search"
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "nas bench 101 towards reproducible neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"
    - "autoqb automl for network quantization and binarization on mobile devices"

Connected through hyperedge HE-5 (induced by source paper "global sparse momentum sgd for pruning very deep neural networks"):
  with members:
    - "towards compact convnets via structure sparsity regularized filter pruning"
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "structured pruning for efficient convnets via incremental regularization"

Connected through hyperedge HE-6 (induced by source paper "taxonomy and evaluation of structured compression of convolutional neural networks"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "the state of sparsity in deep neural networks"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "efficient neural network compression"

Connected through hyperedge HE-7 (induced by source paper "learning sparsity and quantization jointly and automatically for neural network compression via constrained optimization"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "sgdr stochastic gradient descent with restarts"
    - "ternary weight networks"
    - "trained ternary quantization"
    - "continuous control with deep reinforcement 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)