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: "hetconv heterogeneous kernel based convolutions for deep cnns"
  Abstract: We present a novel deep learning architecture in which the convolution operation leverages heterogeneous kernels. The proposed HetConv (Heterogeneous Kernel-Based Convolution) reduces the computation (FLOPs) and the number of parameters as compared to standard convolution operation while still maintaining representational efficiency. To show the effectiveness of our proposed convolution, we present extensive experimental results on the standard convolutional neural network (CNN) architectures such as VGG \cite{vgg2014very} and ResNet \cite{resnet}. We find that after replacing the standard convolutional filters in these architectures with our proposed HetConv filters, we achieve 3X to 8X FLOPs based improvement in speed while still maintaining (and sometimes improving) the accuracy. We also compare our proposed convolutions with group/depth wise convolutions and show that it achieves more FLOPs reduction with significantly higher accuracy.

Connected through hyperedge HE-1 (induced by source paper "cooperative initialization based deep neural network training"):
  with members:
    - "accuracy booster performance boosting using feature map re calibration"
    - "cpwc contextual point wise convolution for object recognition"
    - "play and prune adaptive filter pruning for deep model compression"
    - "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-2 (induced by source paper "training convolutional neural networks with cheap convolutions and online distillation"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "all you need is a few shifts designing efficient convolutional neural networks for image classification"
    - "variational information distillation for knowledge transfer"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "amc automl for model compression and acceleration on mobile devices"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-3 (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"
    - "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"
    - "attention based fully convolutional network for speech emotion recognition"

Connected through hyperedge HE-4 (induced by source paper "gate decorator global filter pruning method for accelerating deep convolutional neural networks"):
  with members:
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "discrimination aware channel pruning for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "deep high resolution representation learning for human pose estimation"
    - "elastic improving cnns with dynamic scaling policies"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multi fiber networks for video recognition"
    - "regularized evolution for image classifier architecture search"
    - "a short note about kinetics 600"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-6 (induced by source paper "a pre defined sparse kernel based convolution for deep cnns"):
  with members:
    - "pre defined sparse neural networks with hardware acceleration"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "exploring the regularity of sparse structure in convolutional neural networks"
    - "incremental network quantization towards lossless cnns with low precision weights"

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"
    - "cpwc contextual point wise convolution for object recognition"
    - "play and prune adaptive filter pruning for deep model compression"
    - "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 "diversifying inference path selection moving mobile network for landmark recognition"):
  with members:
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "a layer decomposition recomposition framework for neuron pruning towards accurate lightweight networks"
    - "extreme network compression via filter group approximation"
    - "skipnet learning dynamic routing in convolutional networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "data driven sparse structure selection for deep neural networks"
    - "blockdrop dynamic inference paths in residual networks"

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)