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: "network decoupling from regular to depthwise separable convolutions"
  Abstract: Depthwise separable convolution has shown great efficiency in network design, but requires time-consuming training procedure with full training-set available. This paper first analyzes the mathematical relationship between regular convolutions and depthwise separable convolutions, and proves that the former one could be approximated with the latter one in closed form. We show depthwise separable convolutions are principal components of regular convolutions. And then we propose network decoupling (ND), a training-free method to accelerate convolutional neural networks (CNNs) by transferring pre-trained CNN models into the MobileNet-like depthwise separable convolution structure, with a promising speedup yet negligible accuracy loss. We further verify through experiments that the proposed method is orthogonal to other training-free methods like channel decomposition, spatial decomposition, etc. Combining the proposed method with them will bring even larger CNN speedup. For instance, ND itself achieves about 2X speedup for the widely used VGG16, and combined with other methods, it reaches 3.7X speedup with graceful accuracy degradation. We demonstrate that ND is widely applicable to classification networks like ResNet, and object detection network like SSD300.

Connected through hyperedge HE-1 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "bigdl a distributed deep learning framework for big data"
    - "deep neural network compression with single and multiple level quantization"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-2 (induced by source paper "sound event detection with depthwise separable and dilated convolutions"):
  with members:
    - "language modelling for sound event detection with teacher forcing and scheduled sampling"
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "listening for sirens locating and classifying acoustic alarms in city scenes"
    - "convolutional recurrent neural networks for bird audio detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "surrey cvssp system for dcase2017 challenge task 4"
    - "stacked convolutional and recurrent neural networks for music emotion recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (induced by source paper "eeg based drowsiness estimation for driving safety using deep q learning"):
  with members:
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "learning representations from eeg with deep recurrent convolutional neural networks"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "depth wise decomposition for accelerating separable convolutions in efficient convolutional neural networks"):
  with members:
    - "mobinet a mobile binary network for image classification"
    - "pack and detect fast object detection in videos using region of interest packing"
    - "feature selective anchor free module for single shot object detection"
    - "prediction tracking segmentation"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bounding box regression with uncertainty for accurate object detection"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-5 (induced by source paper "joint architecture and knowledge distillation in convolutional neural network for offline handwritten chinese text recognition"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "flightnns lightweight quantized deep neural networks for fast and accurate inference"
    - "structured knowledge distillation for dense prediction"
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "writer aware cnn for parsimonious hmm based offline handwritten chinese text recognition"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-6 (induced by source paper "sharpdarts faster and more accurate differentiable architecture search"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "autoaugment learning augmentation policies from data"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "darts differentiable architecture search"
    - "the costar block stacking dataset learning with workspace constraints"

Connected through hyperedge HE-7 (induced by source paper "accelerating training using tensor decomposition"):
  with members:
    - "and the bit goes down revisiting the quantization of neural networks"
    - "lightweight monocular depth estimation model by joint end to end filter pruning"
    - "importance estimation for neural network pruning"
    - "energy and policy considerations for deep learning in nlp"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "rethinking the value of network pruning"
    - "deep gradient compression reducing the communication bandwidth for distributed training"

Connected through hyperedge HE-8 (induced by source paper "composite binary decomposition networks"):
  with members:
    - "quantizing convolutional neural networks for low power high throughput inference engines"
    - "progressive neural architecture search"
    - "binary decomposed dcnn for accelerating computation and compressing model without retraining"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning accurate low bit deep neural networks with stochastic quantization"
    - "dsod learning deeply supervised object detectors from scratch"
    - "the cityscapes dataset for semantic urban scene understanding"

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)