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: "channelnets compact and efficient convolutional neural networks via channel wise convolutions"
  Abstract: Convolutional neural networks (CNNs) have shown great capability of solving various artificial intelligence tasks. However, the increasing model size has raised challenges in employing them in resource-limited applications. In this work, we propose to compress deep models by using channel-wise convolutions, which re- place dense connections among feature maps with sparse ones in CNNs. Based on this novel operation, we build light-weight CNNs known as ChannelNets. Channel- Nets use three instances of channel-wise convolutions; namely group channel-wise convolutions, depth-wise separable channel-wise convolutions, and the convolu- tional classification layer. Compared to prior CNNs designed for mobile devices, ChannelNets achieve a significant reduction in terms of the number of parameters and computational cost without loss in accuracy. Notably, our work represents the first attempt to compress the fully-connected classification layer, which usually accounts for about 25% of total parameters in compact CNNs. Experimental results on the ImageNet dataset demonstrate that ChannelNets achieve consistently better performance compared to prior methods.

Connected through hyperedge HE-1 (induced by source paper "inner imaging networks put lenses into convolutional structure"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dropblock a regularization method for convolutional networks"
    - "convolutional neural networks with alternately updated clique"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "training neural networks using features replay"
    - "gather excite exploiting feature context in convolutional neural networks"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-2 (induced by source paper "neural epitome search for architecture agnostic network compression"):
  with members:
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "low bit quantization of neural networks for efficient inference"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-3 (induced by source paper "eca net efficient channel attention for deep convolutional neural networks"):
  with members:
    - "expectation maximization attention networks for semantic segmentation"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "attention augmented convolutional networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "ccnet criss cross attention for semantic segmentation"
    - "gather excite exploiting feature context in convolutional neural networks"
    - "a 2 nets double attention networks"

Connected through hyperedge HE-4 (induced by source paper "cross channel intragroup sparsity neural network"):
  with members:
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "dnn dataflow choice is overrated"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "block sparse recurrent neural networks"
    - "soft filter pruning for accelerating deep convolutional neural networks"

Connected through hyperedge HE-5 (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"
    - "constructing fast network through deconstruction of convolution"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "inner imaging convolutional networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "convolutional neural networks with alternately updated clique"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "wide residual networks"
    - "xception deep learning with depthwise separable convolutions"
    - "fractalnet ultra deep neural networks without residuals"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "faster r cnn towards real time object detection with region proposal 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)