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: "semantic instance annotation of street scenes by 3d to 2d label transfer"
  Abstract: Semantic annotations are vital for training models for object recognition, semantic segmentation or scene understanding. Unfortunately, pixelwise annotation of images at very large scale is labor-intensive and only little labeled data is available, particularly at instance level and for street scenes. In this paper, we propose to tackle this problem by lifting the semantic instance labeling task from 2D into 3D. Given reconstructions from stereo or laser data, we annotate static 3D scene elements with rough bounding primitives and develop a model which transfers this information into the image domain. We leverage our method to obtain 2D labels for a novel suburban video dataset which we have collected, resulting in 400k semantic and instance image annotations. A comparison of our method to state-of-the-art label transfer baselines reveals that 3D information enables more efficient annotation while at the same time resulting in improved accuracy and time-coherent labels.

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 "a cross season correspondence dataset for robust semantic segmentation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "vlase vehicle localization by aggregating semantic edges"
    - "long term visual localization using semantically segmented images"
    - "image to image translation for domain adaptation"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "cycada cycle consistent adversarial domain adaptation"
    - "road reality oriented adaptation for semantic segmentation of urban scenes"

Connected through hyperedge HE-3 (induced by source paper "semantickitti a dataset for semantic scene understanding of lidar sequences"):
  with members:
    - "efficient semantic scene completion network with spatial group convolution"
    - "voxsegnet volumetric cnns for semantic part segmentation of 3d shapes"
    - "flex convolution million scale point cloud learning beyond grid worlds"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "yolov3 an incremental improvement"
    - "splatnet sparse lattice networks for point cloud processing"
    - "fully convolutional point networks for large scale point clouds"

Connected through hyperedge HE-4 (induced by source paper "u4d unsupervised 4d dynamic scene understanding"):
  with members:
    - "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
    - "4d temporally coherent light field video"
    - "continuous 3d label stereo matching using local expansion moves"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "accurate optical flow via direct cost volume processing"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "towards accurate markerless human shape and pose estimation over time"
    - "joint multi person pose estimation and semantic part segmentation"

Connected through hyperedge HE-5 (induced by source paper "3d bat a semi automatic web based 3d annotation toolbox for full surround multi modal data streams"):
  with members:
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "no blind spots full surround multi object tracking for autonomous vehicles using cameras lidars"
    - "ground plane polling for 6dof pose estimation of objects on the road"
    - "the impact of automatic pre annotation in clinical note data element extraction the clean tool"

Connected through hyperedge HE-6 (induced by source paper "learning 3d shape completion under weak supervision"):
  with members:
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "octnetfusion learning depth fusion from data"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "hierarchical surface prediction for 3d object reconstruction"
    - "shape inpainting using 3d generative adversarial network and recurrent convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "semantic aware grad gan for virtual to real urban scene adaption"):
  with members:
    - "demystifying neural style transfer"
    - "pixelnn example based image synthesis"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "adversarial discriminative domain adaptation"
    - "unsupervised image to image translation networks"
    - "squeeze and excitation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "cereals cost effective region based active learning for semantic segmentation"):
  with members:
    - "adversarial learning for semi supervised semantic segmentation"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "learning from weak and noisy labels for semantic segmentation"

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)