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: "slanted stixels representing san francisco s steepest streets"
  Abstract: In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced that uses an extremely efficient over-segmentation. In doing so, the computational complexity of the Stixel inference algorithm is reduced significantly, achieving real-time computation capabilities with only a slight drop in accuracy. We evaluate the proposed approach in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset.

Connected through hyperedge HE-1 (induced by source paper "virtual kitti 2"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "temporal coherence for active learning in videos"
    - "ga net guided aggregation net for end to end stereo matching"
    - "unsupervised learning of depth and ego motion from video"
    - "playing for benchmarks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-2 (induced by source paper "multimodal end to end autonomous driving"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "towards self supervised high level sensor fusion"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "improved semantic stixels via multimodal sensor fusion"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "deep ordinal regression network for monocular depth estimation"
    - "pointpillars fast encoders for object detection from point clouds"
    - "cirl controllable imitative reinforcement learning for vision based self driving"

Connected through hyperedge HE-3 (induced by source paper "discoman dataset of indoor scenes for odometry mapping and navigation"):
  with members:
    - "scene motion decomposition for learnable visual odometry"
    - "panoptic segmentation"
    - "open3d a modern library for 3d data processing"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "egocentric spatial memory"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "the tum vi benchmark for evaluating visual inertial odometry"

Connected through hyperedge HE-4 (induced by source paper "improved semantic stixels via multimodal sensor fusion"):
  with members:
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "learning from synthetic data addressing domain shift for semantic segmentation"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "the stixel world"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-5 (induced by source paper "real time dense mapping for self driving vehicles using fisheye cameras"):
  with members:
    - "project autovision localization and 3d scene perception for an autonomous vehicle with a multi camera system"
    - "efficient 2d 3d matching for multi camera visual localization"
    - "yolov3 an incremental improvement"
    - "robust dense mapping for large scale dynamic environments"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "instance aware semantic segmentation via multi task network cascades"
    - "high performance and tunable stereo reconstruction"

Connected through hyperedge HE-6 (induced by source paper "the earth ain t flat monocular reconstruction of vehicles on steep and graded roads from a moving camera"):
  with members:
    - "constructing category specific models for monocular object slam"
    - "accurate single stage detector using recurrent rolling convolution"
    - "stacked hourglass networks for human pose estimation"
    - "reconstructing vechicles from a single image shape priors for road scene understanding"
    - "sparseness meets deepness 3d human pose estimation from monocular video"
    - "shapenet an information rich 3d model repository"
    - "towards scene understanding with detailed 3d object representations"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"

Connected through hyperedge HE-7 (induced by source paper "mono stixels monocular depth reconstruction of dynamic street scenes"):
  with members:
    - "the stixel world"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "exploiting semantic information and deep matching for optical flow"
    - "orb slam a versatile and accurate monocular slam system"
    - "very deep convolutional networks for large scale image recognition"
    - "depth map prediction from a single image using a multi scale deep network"
    - "fully convolutional networks 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)