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: "all you need is a few shifts designing efficient convolutional neural networks for image classification"
  Abstract: Shift operation is an efficient alternative over depthwise separable convolution. However, it is still bottlenecked by its implementation manner, namely memory movement. To put this direction forward, a new and novel basic component named Sparse Shift Layer (SSL) is introduced in this paper to construct efficient convolutional neural networks. In this family of architectures, the basic block is only composed by 1x1 convolutional layers with only a few shift operations applied to the intermediate feature maps. To make this idea feasible, we introduce shift operation penalty during optimization and further propose a quantization-aware shift learning method to impose the learned displacement more friendly for inference. Extensive ablation studies indicate that only a few shift operations are sufficient to provide spatial information communication. Furthermore, to maximize the role of SSL, we redesign an improved network architecture to Fully Exploit the limited capacity of neural Network (FE-Net). Equipped with SSL, this network can achieve 75.0% top-1 accuracy on ImageNet with only 563M M-Adds. It surpasses other counterparts constructed by depthwise separable convolution and the networks searched by NAS in terms of accuracy and practical speed.

Connected through hyperedge HE-1 (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"
    - "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"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-2 (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"
    - "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"
    - "searching for accurate binary neural architectures"

Connected through hyperedge HE-3 (induced by source paper "hicore visual hierarchical context reasoning"):
  with members:
    - "connection sensitive attention u net for accurate retinal vessel segmentation"
    - "graph neural networks a review of methods and applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "iterative visual reasoning beyond convolutions"
    - "attention models in graphs a survey"
    - "deep learning on graphs a survey"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-4 (induced by source paper "4 connected shift residual networks"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "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"
    - "constructing fast network through deconstruction of convolution"
    - "learning transferable architectures for scalable image recognition"

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)