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: "foveanet perspective aware urban scene parsing"
  Abstract: Parsing urban scene images benefits many applications, especially self-driving. Most of the current solutions employ generic image parsing models that treat all scales and locations in the images equally and do not consider the geometry property of car-captured urban scene images. Thus, they suffer from heterogeneous object scales caused by perspective projection of cameras on actual scenes and inevitably encounter parsing failures on distant objects as well as other boundary and recognition errors. In this work, we propose a new FoveaNet model to fully exploit the perspective geometry of scene images and address the common failures of generic parsing models. FoveaNet estimates the perspective geometry of a scene image through a convolutional network which integrates supportive evidence from contextual objects within the image. Based on the perspective geometry information, FoveaNet "undoes" the camera perspective projection analyzing regions in the space of the actual scene, and thus provides much more reliable parsing results. Furthermore, to effectively address the recognition errors, FoveaNet introduces a new dense CRFs model that takes the perspective geometry as a prior potential. We evaluate FoveaNet on two urban scene parsing datasets, Cityspaces and CamVid, which demonstrates that FoveaNet can outperform all the well-established baselines and provide new state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "cars can t fly up in the sky improving urban scene segmentation via height driven attention networks"):
  with members:
    - "acfnet attentional class feature network for semantic segmentation"
    - "asymmetric non local neural networks for semantic segmentation"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "efficient segmentation learning downsampling near semantic boundaries"
    - "boundary aware feature propagation for scene segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning a discriminative feature network for semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-2 (induced by source paper "learning when and where to zoom with deep reinforcement learning"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "efficient object detection in large images using deep reinforcement learning"
    - "learning to interpret satellite images using wikipedia"
    - "dropblock a regularization method for convolutional networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-3 (induced by source paper "consensus feature network for scene parsing"):
  with members:
    - "asymmetric non local neural networks for semantic segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "adaptive context network for scene 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"
    - "adaptive affinity fields for semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "learning a layout transfer network for context aware object detection"):
  with members:
    - "sinet a scale insensitive convolutional neural network for fast vehicle detection"
    - "learning to look around objects for top view representations of outdoor scenes"
    - "a general pipeline for 3d detection of vehicles"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "seeing what is not there learning context to determine where objects are missing"
    - "dsod learning deeply supervised object detectors from scratch"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"
    - "accurate single stage detector using recurrent rolling convolution"

Connected through hyperedge HE-5 (induced by source paper "boosting real time driving scene parsing with shared semantics"):
  with members:
    - "short term prediction and multi camera fusion on semantic grids"
    - "sfnet learning object aware semantic correspondence"
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "learning correspondence from the cycle consistency of time"
    - "searching for mobilenetv3"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "dynamic video segmentation network"

Connected through hyperedge HE-6 (induced by source paper "urban i from urban scenes to mapping slums transport modes and pedestrians in cities using deep learning and computer vision"):
  with members:
    - "midgar detection of people through computer vision in the internet of things scenarios to improve the security in smart cities smart towns and smart homes"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "rethinking atrous convolution for semantic image segmentation"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "densely connected convolutional networks"
    - "deep learning the city quantifying urban perception at a global scale"
    - "pyramid scene parsing network"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "weathernet recognising weather and visual conditions from street level images using deep residual learning"):
  with members:
    - "urban i from urban scenes to mapping slums transport modes and pedestrians in cities using deep learning and computer vision"
    - "a cnn rnn architecture for multi label weather recognition"
    - "weather classification a new multi class dataset data augmentation approach and comprehensive evaluations of convolutional neural networks"
    - "linknet exploiting encoder representations for efficient semantic segmentation"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (induced by source paper "tree structured kronecker convolutional network for semantic segmentation"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "cgnet a light weight context guided network for semantic segmentation"
    - "context encoding for semantic segmentation"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "spatial as deep spatial cnn for traffic scene understanding"

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)