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: "the earth ain t flat monocular reconstruction of vehicles on steep and graded roads from a moving camera"
  Abstract: Accurate localization of other traffic participants is a vital task in autonomous driving systems. State-of-the-art systems employ a combination of sensing modalities such as RGB cameras and LiDARs for localizing traffic participants, but most such demonstrations have been confined to plain roads. We demonstrate, to the best of our knowledge, the first results for monocular object localization and shape estimation on surfaces that do not share the same plane with the moving monocular camera. We approximate road surfaces by local planar patches and use semantic cues from vehicles in the scene to initialize a local bundle-adjustment like procedure that simultaneously estimates the pose and shape of the vehicles, and the orientation of the local ground plane on which the vehicle stands as well. We evaluate the proposed approach on the KITTI and SYNTHIA-SF benchmarks, for a variety of road plane configurations. The proposed approach significantly improves the state-of-the-art for monocular object localization on arbitrarily-shaped roads.

Connected through hyperedge HE-1 (induced by source paper "rodnet object detection under severe conditions using vision radio cross modal supervision"):
  with members:
    - "video instance segmentation"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "centernet keypoint triplets for object detection"
    - "tracking without bells and whistles"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "moana an online learned adaptive appearance model for robust multiple object tracking in 3d"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (induced by source paper "multi object monocular slam for dynamic environments"):
  with members:
    - "cubeslam monocular 3 d object slam"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "mask r cnn"
    - "stacked hourglass networks for human pose estimation"
    - "yolo9000 better faster stronger"
    - "reconstructing vechicles from a single image shape priors for road scene understanding"

Connected through hyperedge HE-3 (induced by source paper "hierarchical metric learning and matching for 2d and 3d geometric correspondences"):
  with members:
    - "deep supervision with intermediate concepts"
    - "learning spread out local feature descriptors"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "sfm net learning of structure and motion from video"
    - "semantic texture for robust dense tracking"
    - "lift learned invariant feature transform"

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.RO(Robotics)