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: "efficient dense modules of asymmetric convolution for real time semantic segmentation"
  Abstract: Real-time semantic segmentation plays an important role in practical applications such as self-driving and robots. Most semantic segmentation research focuses on improving estimation accuracy with little consideration on efficiency. Several previous studies that emphasize high-speed inference often fail to produce high-accuracy segmentation results. In this paper, we propose a novel convolutional network named Efficient Dense modules with Asymmetric convolution (EDANet), which employs an asymmetric convolution structure and incorporates dilated convolution and dense connectivity to achieve high efficiency at low computational cost and model size. EDANet is 2.7 times faster than the existing fast segmentation network, ICNet, while it achieves a similar mIoU score without any additional context module, post-processing scheme, and pretrained model. We evaluate EDANet on Cityscapes and CamVid datasets, and compare it with the other state-of-art systems. Our network can run with the high-resolution inputs at the speed of 108 FPS on one GTX 1080Ti.

Connected through hyperedge HE-1 (induced by source paper "spatial sampling network for fast scene understanding"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "benchmark analysis of representative deep neural network architectures"
    - "guided upsampling network for real time semantic segmentation"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "fast scene understanding for autonomous driving"

Connected through hyperedge HE-2 (induced by source paper "squeezenas fast neural architecture search for faster semantic segmentation"):
  with members:
    - "embench quantifying performance variations of deep neural networks across modern commodity devices"
    - "searching for mobilenetv3"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "bayesian meta network architecture learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"

Connected through hyperedge HE-3 (induced by source paper "esfnet efficient network for building extraction from high resolution aerial images"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "exfuse enhancing feature fusion for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "interleaved group convolutions for deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "choicenet cnn learning through choice of multiple feature map representations"):
  with members:
    - "adaptive affinity fields for semantic segmentation"
    - "recurrent scene parsing with perspective understanding in the loop"
    - "understanding convolution for semantic segmentation"
    - "residual conv deconv grid network for semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "resnet in resnet generalizing residual architectures"
    - "deep networks with stochastic depth"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "acnet strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks"):
  with members:
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "learning efficient convolutional networks through network slimming"
    - "learning transferable architectures for scalable image recognition"
    - "thinet a filter level pruning method for deep neural network compression"
    - "squeeze and excitation networks"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-6 (induced by source paper "dsnet an efficient cnn for road scene segmentation"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "ds pass detail sensitive panoramic annular semantic segmentation through swaftnet for surrounding sensing"):
  with members:
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "cross view semantic segmentation for sensing surroundings"
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "orientation aware semantic segmentation on icosahedron spheres"
    - "fisheyemodnet moving object detection on surround view cameras for autonomous driving"
    - "a comparative study of high recall real time semantic segmentation based on swift factorized network"
    - "bisenet bilateral segmentation network for real time semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "boundary aware network for fast and high accuracy portrait segmentation"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "exfuse enhancing feature fusion for semantic segmentation"
    - "semantic human matting"
    - "fast deep matting for portrait animation on mobile phone"
    - "real time deep hair matting on mobile devices"
    - "deep image matting"
    - "squeeze and excitation 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)