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: "birdnet a 3d object detection framework from lidar information"
  Abstract: Understanding driving situations regardless the conditions of the traffic scene is a cornerstone on the path towards autonomous vehicles; however, despite common sensor setups already include complementary devices such as LiDAR or radar, most of the research on perception systems has traditionally focused on computer vision. We present a LiDAR-based 3D object detection pipeline entailing three stages. First, laser information is projected into a novel cell encoding for bird's eye view projection. Later, both object location on the plane and its heading are estimated through a convolutional neural network originally designed for image processing. Finally, 3D oriented detections are computed in a post-processing phase. Experiments on KITTI dataset show that the proposed framework achieves state-of-the-art results among comparable methods. Further tests with different LiDAR sensors in real scenarios assess the multi-device capabilities of the approach.

Connected through hyperedge HE-1 (induced by source paper "rtm3d real time monocular 3d detection from object keypoints for autonomous driving"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "foveabox beyond anchor based object detector"
    - "pixor real time 3d object detection from point clouds"
    - "mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors"
    - "shift r cnn deep monocular 3d object detection with closed form geometric constraints"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "cubeslam monocular 3 d object slam"

Connected through hyperedge HE-2 (induced by source paper "cae lo lidar odometry leveraging fully unsupervised convolutional auto encoder for interest point detection and feature description"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "deepicp an end to end deep neural network for 3d point cloud registration"
    - "key net keypoint detection by handcrafted and learned cnn filters"
    - "lo net deep real time lidar odometry"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"

Connected through hyperedge HE-3 (induced by source paper "3d object detection from lidar data using distance depended feature extraction"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "from points to parts 3d object detection from point cloud with part aware and part aggregation network"
    - "pixor real time 3d object detection from point clouds"
    - "range adaptation for 3d object detection in lidar"
    - "a baseline for 3d multi object tracking"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"

Connected through hyperedge HE-4 (induced by source paper "pointtracknet an end to end network for 3 d object detection and tracking from point clouds"):
  with members:
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "pointit a fast tracking framework based on 3d instance segmentation"
    - "autotrack a lightweight object detection and tracking system for the sae autodrive challenge"
    - "fantrack 3d multi object tracking with feature association network"
    - "end to end learning of multi sensor 3d tracking by detection"
    - "flownet3d learning scene flow in 3d point clouds"
    - "focal loss in 3d object detection"
    - "pointrcnn 3d object proposal generation and detection from point cloud"

Connected through hyperedge HE-5 (induced by source paper "sianms non maximum suppression with siamese networks for multi camera 3d object detection"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "scalability in perception for autonomous driving waymo open dataset"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "m3d rpn monocular 3d region proposal network for object detection"
    - "single stage monocular 3d object detection with virtual cameras"
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"
    - "joint 3d proposal generation and object detection from view aggregation"

Connected through hyperedge HE-6 (induced by source paper "single stage object detection from top view grid maps on custom sensor setups"):
  with members:
    - "scl towards accurate domain adaptive object detection via gradient detach based stacked complementary losses"
    - "pixor real time 3d object detection from point clouds"
    - "capturing object detection uncertainty in multi layer grid maps"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "pointpillars fast encoders for object detection from point clouds"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "strong weak distribution alignment for adaptive object detection"
    - "complex yolo real time 3d object detection on point clouds"

Connected through hyperedge HE-7 (induced by source paper "birdnet end to end 3d object detection in lidar bird s eye view"):
  with members:
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "pointpillars fast encoders for object detection from point clouds"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "feature pyramid networks for object detection"
    - "multi view 3d object detection network for autonomous driving"

Connected through hyperedge HE-8 (induced by source paper "cooperative lidar object detection via feature sharing in deep networks"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "carla an open urban driving simulator"
    - "vehicle detection from 3d lidar using fully convolutional network"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "adaptive content control for communication amongst cooperative automated vehicles"

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)