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: "shufflenet an extremely efficient convolutional neural network for mobile devices"
  Abstract: We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet on ImageNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves ~13x actual speedup over AlexNet while maintaining comparable accuracy.

Connected through hyperedge HE-1 (induced by source paper "post training piecewise linear quantization for deep neural networks"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "low bit quantization of neural networks for efficient inference"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "fighting quantization bias with bias"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "stochastic activation pruning for robust adversarial defense"

Connected through hyperedge HE-2 (induced by source paper "attention guided chained context aggregation for semantic segmentation"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "selective kernel networks"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "learning architectures for binary networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "binarized neural architecture search"
    - "mobinet a mobile binary network for image classification"
    - "bayesian optimized 1 bit cnns"
    - "searching for a robust neural architecture in four gpu hours"
    - "exploring randomly wired neural networks for image recognition"
    - "xnor net improved binary neural networks"

Connected through hyperedge HE-4 (induced by source paper "highly efficient salient object detection with 100k parameters"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "linear context transform block"
    - "searching for mobilenetv3"
    - "a simple pooling based design for real time salient object detection"
    - "dabnet depth wise asymmetric bottleneck for real time semantic segmentation"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "deeply supervised salient object detection with short connections"

Connected through hyperedge HE-5 (induced by source paper "bats binary architecture search"):
  with members:
    - "learning architectures for binary networks"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "back to simplicity how to train accurate bnns from scratch"
    - "regularizing activation distribution for training binarized deep networks"
    - "accurate and compact convolutional neural networks with trained binarization"
    - "xnor net improved binary neural networks"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"

Connected through hyperedge HE-6 (induced by source paper "real time high performance semantic image segmentation of urban street scenes"):
  with members:
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "lednet a lightweight encoder decoder network for real time semantic segmentation"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "vortex pooling improving context representation in semantic segmentation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "yolov3 an incremental improvement"
    - "light weight refinenet for real time semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "crl class representative learning for image classification"):
  with members:
    - "unifying unsupervised domain adaptation and zero shot visual recognition"
    - "meta transfer networks for zero shot learning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "rethinking imagenet pre training"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "towards universal representation for unseen action recognition"
    - "complementary attributes a new clue to zero shot learning"

Connected through hyperedge HE-8 (induced by source paper "a network pruning network approach to deep model compression"):
  with members:
    - "cooperative initialization based deep neural network training"
    - "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"
    - "amc automl for model compression and acceleration on mobile devices"
    - "leveraging filter correlations for deep model compression"

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)