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: "torontocity seeing the world with a million eyes"
  Abstract: In this paper we introduce the TorontoCity benchmark, which covers the full greater Toronto area (GTA) with 712.5 $km^2$ of land, 8439 $km$ of road and around 400,000 buildings. Our benchmark provides different perspectives of the world captured from airplanes, drones and cars driving around the city. Manually labeling such a large scale dataset is infeasible. Instead, we propose to utilize different sources of high-precision maps to create our ground truth. Towards this goal, we develop algorithms that allow us to align all data sources with the maps while requiring minimal human supervision. We have designed a wide variety of tasks including building height estimation (reconstruction), road centerline and curb extraction, building instance segmentation, building contour extraction (reorganization), semantic labeling and scene type classification (recognition). Our pilot study shows that most of these tasks are still difficult for modern convolutional neural networks.

Connected through hyperedge HE-1 (induced by source paper "the apolloscape open dataset for autonomous driving and its application"):
  with members:
    - "deep stochastic attraction and repulsion embedding for image based localization"
    - "stochastic attraction repulsion embedding for large scale image localization"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "deepmvs learning multi view stereopsis"
    - "apollocar3d a large 3d car instance understanding benchmark for autonomous driving"
    - "dels 3d deep localization and segmentation with a 3d semantic map"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-2 (induced by source paper "nuscenes a multimodal dataset for autonomous driving"):
  with members:
    - "how much real data do we actually need analyzing object detection performance using synthetic and real data"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "starnet targeted computation for object detection in point clouds"
    - "spatially aware graph neural networks for relational behavior forecasting from sensor data"
    - "d 2 city a large scale dashcam video dataset of diverse traffic scenarios"
    - "disentangling monocular 3d object detection"
    - "a baseline for 3d multi object tracking"
    - "physics based rendering for improving robustness to rain"

Connected through hyperedge HE-3 (induced by source paper "a relation augmented fully convolutional network for semantic segmentation in aerial scenes"):
  with members:
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "adaptive affinity fields for semantic segmentation"
    - "switchable temporal propagation network"
    - "relation networks for object detection"
    - "a simple neural network module for relational reasoning"
    - "learning affinity via spatial propagation networks"

Connected through hyperedge HE-4 (induced by source paper "auxnet auxiliary tasks enhanced semantic segmentation for automated driving"):
  with members:
    - "effnet an efficient structure for convolutional neural networks"
    - "monocular fisheye camera depth estimation using sparse lidar supervision"
    - "auxiliary tasks in multi task learning"
    - "driving scene perception network real time joint detection depth estimation and semantic segmentation"
    - "end to end multi task learning with attention"
    - "multi task learning as multi objective optimization"
    - "monocular depth estimation by learning from heterogeneous datasets"
    - "synscapes a photorealistic synthetic dataset for street scene parsing"

Connected through hyperedge HE-5 (induced by source paper "argoverse 3d tracking and forecasting with rich maps"):
  with members:
    - "deep multi sensor lane detection"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "flownet3d learning scene flow in 3d point clouds"
    - "pointpillars fast encoders for object detection from point clouds"

Connected through hyperedge HE-6 (induced by source paper "neural turtle graphics for modeling city road layouts"):
  with members:
    - "darnet deep active ray network for building segmentation"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "fast interactive object annotation with curve gcn"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"

Connected through hyperedge HE-7 (induced by source paper "end to end trainable active contours via differentiable rendering"):
  with members:
    - "cnn based semantic segmentation using level set loss"
    - "darnet deep active ray network for building segmentation"
    - "mesh r cnn"
    - "fast interactive object annotation with curve gcn"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "improved breast mass segmentation in mammograms with conditional residual u net"
    - "breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network"
    - "unsupervised learning of shape and pose with differentiable point clouds"

Connected through hyperedge HE-8 (induced by source paper "building segmentation through a gated graph convolutional neural network with deep structured feature embedding"):
  with members:
    - "building footprint generation using improved generative adversarial networks"
    - "temporary removal aerial imagery for roof segmentation a large scale dataset towards automatic mapping of buildings"
    - "a feature embedding strategy for high level cnn representations from multiple convnets"
    - "gated graph sequence neural networks"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

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)