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: "espnetv2 a light weight power efficient and general purpose convolutional neural network"
  Abstract: We introduce a light-weight, power efficient, and general purpose convolutional neural network, ESPNetv2, for modeling visual and sequential data. Our network uses group point-wise and depth-wise dilated separable convolutions to learn representations from a large effective receptive field with fewer FLOPs and parameters. The performance of our network is evaluated on four different tasks: (1) object classification, (2) semantic segmentation, (3) object detection, and (4) language modeling. Experiments on these tasks, including image classification on the ImageNet and language modeling on the PenTree bank dataset, demonstrate the superior performance of our method over the state-of-the-art methods. Our network outperforms ESPNet by 4-5% and has 2-4x fewer FLOPs on the PASCAL VOC and the Cityscapes dataset. Compared to YOLOv2 on the MS-COCO object detection, ESPNetv2 delivers 4.4% higher accuracy with 6x fewer FLOPs. Our experiments show that ESPNetv2 is much more power efficient than existing state-of-the-art efficient methods including ShuffleNets and MobileNets. Our code is open-source and available at this https URL

Connected through hyperedge HE-1 (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-2 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost"):
  with members:
    - "adaptative inference cost with convolutional neural mixture models"
    - "depth adaptive transformer"
    - "hydra an ensemble of convolutional neural networks for geospatial land classification"
    - "robust training with ensemble consensus"
    - "deep tree learning for zero shot face anti spoofing"
    - "improved techniques for training adaptive deep networks"
    - "searching for mobilenetv3"
    - "universally slimmable networks and improved training techniques"

Connected through hyperedge HE-3 (induced by source paper "least squares binary quantization of neural networks"):
  with members:
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-4 (induced by source paper "farsee net real time semantic segmentation by efficient multi scale context aggregation and feature space super resolution"):
  with members:
    - "lednet a lightweight encoder decoder network for real time semantic segmentation"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "high resolution representations for labeling pixels and regions"
    - "fast scnn fast semantic segmentation network"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "guided upsampling network for real time semantic segmentation"
    - "contextnet exploring context and detail for semantic segmentation in real time"

Connected through hyperedge HE-5 (induced by source paper "semantic flow for fast and accurate scene parsing"):
  with members:
    - "expectation maximization attention networks for semantic segmentation"
    - "customizable architecture search for semantic segmentation"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "panoptic feature pyramid networks"
    - "ocnet object context network for scene parsing"
    - "guided upsampling network for real time semantic segmentation"
    - "low latency video semantic segmentation"
    - "fast neural architecture search of compact semantic segmentation models via auxiliary cells"

Connected through hyperedge HE-6 (induced by source paper "depthwise stft based separable convolutional neural networks"):
  with members:
    - "lp 3dcnn unveiling local phase in 3d convolutional neural networks"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "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"
    - "don t decay the learning rate increase the batch size"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-7 (induced by source paper "towards explainable bit error tolerance of resistive ram based binarized neural networks"):
  with members:
    - "outstanding bit error tolerance of resistive ram based binarized neural networks"
    - "bmxnet an open source binary neural network implementation based on mxnet"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fixed point quantization of deep convolutional networks"
    - "deep learning with limited numerical precision"
    - "low precision storage for deep learning"

Connected through hyperedge HE-8 (induced by source paper "dicenet dimension wise convolutions for efficient networks"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "searching for mobilenetv3"
    - "mixconv mixed depthwise convolutional kernels"
    - "discovering neural wirings"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

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)