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: "enet a deep neural network architecture for real time semantic segmentation"
  Abstract: The ability to perform pixel-wise semantic segmentation in real-time is of paramount importance in practical mobile applications. Recent deep neural networks aimed at this task have the disadvantage of requiring a large number of floating point operations and have long run-times that hinder their usability. In this paper, we propose a novel deep neural network architecture named ENet (efficient neural network), created specifically for tasks requiring low latency operation. ENet is up to 18x faster, requires 75x less FLOPs, has 79x less parameters, and provides similar or better accuracy to existing models.           #N#We have tested it on CamVid, Cityscapes and SUN datasets and report on comparisons with existing state-of-the-art methods, and the trade-offs between accuracy and processing time of a network. We present performance measurements of the proposed architecture on embedded systems and suggest possible software improvements that could make ENet even faster.

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 "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-3 (induced by source paper "unipose unified human pose estimation in single images and videos"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "cascade feature aggregation for human pose estimation"
    - "human pose estimation with spatial contextual information"
    - "deep high resolution representation learning for human pose estimation"
    - "lighttrack a generic framework for online top down human pose tracking"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "multi scale structure aware network for human pose estimation"

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"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "low latency video semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "efficient semantic video segmentation with per frame inference"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "deep video inpainting"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "high resolution representations for labeling pixels and regions"
    - "deep high resolution representation learning for human pose estimation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dynamic video segmentation network"

Connected through hyperedge HE-7 (induced by source paper "building footprint generation by integratingconvolution neural network with feature pairwiseconditional random field fpcrf"):
  with members:
    - "a framework for large scale mapping of human settlement extent from sentinel 2 images via fully convolutional neural networks"
    - "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"
    - "correcting rural building annotations in openstreetmap using convolutional neural networks"
    - "pixel adaptive convolutional neural networks"
    - "relation network for multi label aerial image classification"
    - "building footprint generation using improved generative adversarial networks"
    - "building segmentation through a gated graph convolutional neural network with deep structured feature embedding"
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"

Connected through hyperedge HE-8 (induced by source paper "monolayout amodal scene layout from a single image"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "wise ale wide sample estimator for approximate latent embedding"
    - "monocular semantic occupancy grid mapping with convolutional variational encoder decoder networks"
    - "pixor real time 3d object detection from point clouds"
    - "learning 2d to 3d lifting for object detection in 3d for autonomous vehicles"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "learning to look around objects for top view representations of outdoor scenes"

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)