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: "approximated oracle filter pruning for destructive cnn width optimization"
  Abstract: It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i.e., the width) at each layer is tricky, given an architecture; and 2) the computational intensity of CNNs impedes the deployment on computationally limited devices. Oracle Pruning is designed to remove the unimportant filters from a well-trained CNN, which estimates the filters' importance by ablating them in turn and evaluating the model, thus delivers high accuracy but suffers from intolerable time complexity, and requires a given resulting width but cannot automatically find it. To address these problems, we propose Approximated Oracle Filter Pruning (AOFP), which keeps searching for the least important filters in a binary search manner, makes pruning attempts by masking out filters randomly, accumulates the resulting errors, and finetunes the model via a multi-path framework. As AOFP enables simultaneous pruning on multiple layers, we can prune an existing very deep CNN with acceptable time cost, negligible accuracy drop, and no heuristic knowledge, or re-design a model which exerts higher accuracy and faster inference.

Connected through hyperedge HE-1 (induced by source paper "towards efficient model compression via learned global ranking"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "regularizing activation distribution for training binarized deep networks"
    - "adascale towards real time video object detection using adaptive scaling"
    - "importance estimation for neural network pruning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mavbench micro aerial vehicle benchmarking"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"

Connected through hyperedge HE-2 (induced by source paper "knapsack pruning with inner distillation"):
  with members:
    - "network pruning via transformable architecture search"
    - "contrastive representation distillation"
    - "importance estimation for neural network pruning"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "convolutional neural network pruning using filter attenuation"):
  with members:
    - "modeling of pruning techniques for deep neural networks simplification"
    - "learning to prune filters in convolutional neural networks"
    - "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-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"
    - "learning to prune filters in convolutional neural networks"
    - "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 "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"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "searching for mobilenetv3"
    - "single path one shot neural architecture search with uniform sampling"

Connected through hyperedge HE-6 (induced by source paper "legr filter pruning via learned global ranking"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "adascale towards real time video object detection using adaptive scaling"
    - "transformer xl attentive language models beyond a fixed length context"
    - "habitat a platform for embodied ai research"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"

Connected through hyperedge HE-7 (induced by source paper "global sparse momentum sgd for pruning very deep neural networks"):
  with members:
    - "metapruning meta learning for automatic neural network channel pruning"
    - "towards compact convnets via structure sparsity regularized filter pruning"
    - "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-8 (induced by source paper "acnet strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks"):
  with members:
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "learning efficient convolutional networks through network slimming"
    - "learning transferable architectures for scalable image recognition"
    - "thinet a filter level pruning method for deep neural network compression"
    - "squeeze and excitation networks"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

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.LG(Machine Learning)