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: "clcnet improving the efficiency of convolutional neural network using channel local convolutions"
  Abstract: Depthwise convolution and grouped convolution has been successfully applied to improve the efficiency of convolutional neural network (CNN). We suggest that these models can be considered as special cases of a generalized convolution operation, named channel local convolution(CLC), where an output channel is computed using a subset of the input channels. This definition entails computation dependency relations between input and output channels, which can be represented by a channel dependency graph(CDG). By modifying the CDG of grouped convolution, a new CLC kernel named interlaced grouped convolution (IGC) is created. Stacking IGC and GC kernels results in a convolution block (named CLC Block) for approximating regular convolution. By resorting to the CDG as an analysis tool, we derive the rule for setting the meta-parameters of IGC and GC and the framework for minimizing the computational cost. A new CNN model named clcNet is then constructed using CLC blocks, which shows significantly higher computational efficiency and fewer parameters compared to state-of-the-art networks, when being tested using the ImageNet-1K dataset. Source code is available at this https URL .

Connected through hyperedge HE-1 (induced by source paper "interlaced sparse self attention for semantic segmentation"):
  with members:
    - "deeperlab single shot image parser"
    - "generating long sequences with sparse transformers"
    - "semantic correlation promoted shape variant context for segmentation"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "devil in the details towards accurate single and multiple human parsing"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "mobisr efficient on device super resolution through heterogeneous mobile processors"):
  with members:
    - "embench quantifying performance variations of deep neural networks across modern commodity devices"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "deploying deep neural networks in the embedded space"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "pirm challenge on perceptual image enhancement on smartphones report"
    - "fast and accurate single image super resolution via information distillation network"
    - "effnet an efficient structure for convolutional neural networks"
    - "image super resolution using very deep residual channel attention networks"

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"
    - "channelnets compact and efficient convolutional neural networks via channel wise convolutions"
    - "gather excite exploiting feature context in convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "antnets mobile convolutional neural networks for resource efficient image classification"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeeze and excitation networks"
    - "the power of sparsity in convolutional neural networks"
    - "condensenet an efficient densenet using learned group convolutions"
    - "aggregated residual transformations for deep neural 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)