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: "constructing fast network through deconstruction of convolution"
  Abstract: Convolutional neural networks have achieved great success in various vision tasks; however, they incur heavy resource costs. By using deeper and wider networks, network accuracy can be improved rapidly. However, in an environment with limited resources (e.g., mobile applications), heavy networks may not be usable. This study shows that naive convolution can be deconstructed into a shift operation and pointwise convolution. To cope with various convolutions, we propose a new shift operation called active shift layer (ASL) that formulates the amount of shift as a learnable function with shift parameters. This new layer can be optimized end-to-end through backpropagation and it can provide optimal shift values. Finally, we apply this layer to a light and fast network that surpasses existing state-of-the-art networks.

Connected through hyperedge HE-1 (induced by source paper "towards the systematic reporting of the energy and carbon footprints of machine learning"):
  with members:
    - "tackling climate change with machine learning"
    - "pay less attention with lightweight and dynamic convolutions"
    - "accelerating deep learning by focusing on the biggest losers"
    - "hardnet a low memory traffic network"
    - "energy and policy considerations for deep learning in nlp"
    - "quantifying the carbon emissions of machine learning"
    - "single headed attention rnn stop thinking with your head"
    - "green ai"

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"
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "ghostnet more features from cheap operations"):
  with members:
    - "learning instance wise sparsity for accelerating deep models"
    - "autogan neural architecture search for generative adversarial networks"
    - "all you need is a few shifts designing efficient convolutional neural networks for image classification"
    - "e2 train training state of the art cnns with over 80 energy savings"
    - "fasterseg searching for faster real time semantic segmentation"
    - "searching for mobilenetv3"
    - "data free learning of student networks"
    - "importance estimation for neural network pruning"

Connected through hyperedge HE-4 (induced by source paper "neural similarity learning"):
  with members:
    - "compressive hyperspherical energy minimization"
    - "implicit regularization in deep matrix factorization"
    - "a closer look at few shot classification"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "decoupled networks"
    - "meta learning with latent embedding optimization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "towards understanding the role of over parametrization in generalization of neural networks"

Connected through hyperedge HE-5 (induced by source paper "all you need is a few shifts designing efficient convolutional neural networks for image classification"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a layer decomposition recomposition framework for neuron pruning towards accurate lightweight networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "darts differentiable architecture search"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "progressive neural architecture search"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-6 (induced by source paper "attention based pruning for shift networks"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "learning to prune filters in convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "quantized guided pruning for efficient hardware implementations of convolutional neural networks"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "nisp pruning networks using neuron importance score propagation"
    - "shift a zero flop zero parameter alternative to spatial convolutions"

Connected through hyperedge HE-7 (induced by source paper "4 connected shift residual networks"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "all you need is a few shifts designing efficient convolutional neural networks for image classification"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "residual networks of residual networks multilevel residual networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "benchmark analysis of representative deep neural network architectures"
    - "motion feature network fixed motion filter for action recognition"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-8 (induced by source paper "full stack filters to build minimum viable cnns"):
  with members:
    - "learning implicitly recurrent cnns through parameter sharing"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimmable neural networks"
    - "channelnets compact and efficient convolutional neural networks via channel wise convolutions"
    - "rethinking the value of network pruning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "group equivariant convolutional 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)