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: "scalability in perception for autonomous driving waymo open dataset"
  Abstract: The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data. Existing self-driving datasets are limited in the scale and variation of the environments they capture, even though generalization within and between operating regions is crucial to the overall viability of the technology. In an effort to help align the research community's contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset. Our new dataset consists of 1150 scenes that each span 20 seconds, consisting of well synchronized and calibrated high quality LiDAR and camera data captured across a range of urban and suburban geographies. It is 15x more diverse than the largest camera+LiDAR dataset available based on our proposed diversity metric. We exhaustively annotated this data with 2D (camera image) and 3D (LiDAR) bounding boxes, with consistent identifiers across frames. Finally, we provide strong baselines for 2D as well as 3D detection and tracking tasks. We further study the effects of dataset size and generalization across geographies on 3D detection methods. Find data, code and more up-to-date information at this http URL.

Connected through hyperedge HE-1 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving"):
  with members:
    - "trajectory forecasts in unknown environments conditioned on grid based plans"
    - "urban driving with conditional imitation learning"
    - "grip graph based interaction aware trajectory prediction"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "multiple futures prediction"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "conditional vehicle trajectories prediction in carla urban environment"
    - "on the variance of the adaptive learning rate and beyond"

Connected through hyperedge HE-2 (induced by source paper "confidence guided stereo 3d object detection with split depth estimation"):
  with members:
    - "dsgn deep stereo geometry network for 3d object detection"
    - "canadian adverse driving conditions dataset"
    - "object centric stereo matching for 3d object detection"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "pv rcnn point voxel feature set abstraction for 3d object detection"
    - "task aware monocular depth estimation for 3d object detection"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "triangulation learning network from monocular to stereo 3d object detection"

Connected through hyperedge HE-3 (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"
    - "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"
    - "birdnet a 3d object detection framework from lidar information"
    - "joint 3d proposal generation and object detection from view aggregation"

Connected through hyperedge HE-4 (induced by source paper "a benchmark for lidar based panoptic segmentation based on kitti"):
  with members:
    - "jsis3d joint semantic instance segmentation of 3d point clouds with multi task pointwise networks and multi value conditional random fields"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "panoptic segmentation"
    - "an end to end network for panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "seamless scene segmentation"
    - "kpconv flexible and deformable convolution for point clouds"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-5 (induced by source paper "an lstm based autonomous driving model using waymo open dataset"):
  with members:
    - "end to end learning for self driving cars"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-6 (induced by source paper "canadian adverse driving conditions dataset"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "a 3d dataset towards autonomous driving in challenging environments"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-7 (induced by source paper "libre the multiple 3d lidar dataset"):
  with members:
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-8 (induced by source paper "eu long term dataset with multiple sensors for autonomous driving"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "multisensor online transfer learning for 3d lidar based human classification with a mobile robot"
    - "artificial intelligence for long term robot autonomy a survey"
    - "3dof pedestrian trajectory prediction learned from long term autonomous mobile robot deployment data"
    - "segmatch segment based place recognition in 3d point clouds"
    - "the cityscapes dataset for semantic urban scene understanding"

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)