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: "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
  Abstract: Recent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually equipped with different sensors (e.g. cameras, LiDARs, Radars), and multiple sensing modalities can be fused to exploit their complementary properties. In this context, many methods have been proposed for deep multi-modal perception problems. However, there is no general guideline for network architecture design, and questions of "what to fuse", "when to fuse", and "how to fuse" remain open. This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi-modal object detection and semantic segmentation in autonomous driving. To this end, we first provide an overview of on-board sensors on test vehicles, open datasets, and background information for object detection and semantic segmentation in autonomous driving research. We then summarize the fusion methodologies and discuss challenges and open questions. In the appendix, we provide tables that summarize topics and methods. We also provide an interactive online platform to navigate each reference: this https URL.

Connected through hyperedge HE-1 (induced by source paper "leveraging uncertainties for deep multi modal object detection in autonomous driving"):
  with members:
    - "deep evidential regression"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "learning multimodal fixed point weights using gradient descent"
    - "pixor real time 3d object detection from point clouds"
    - "deep active learning for efficient training of a lidar 3d object detector"
    - "learning an uncertainty aware object detector for autonomous driving"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "nuscenes a multimodal dataset for autonomous driving"

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"
    - "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"
    - "sips succinct interest points from unsupervised inlierness probability learning"

Connected through hyperedge HE-3 (induced by source paper "au air a multi modal unmanned aerial vehicle dataset for low altitude traffic surveillance"):
  with members:
    - "autonomous aerial cinematography in unstructured environments with learned artistic decision making"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "vision meets drones a challenge"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the blackbird dataset a large scale dataset for uav perception in aggressive flight"
    - "yolov3 an incremental improvement"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "the highd dataset a drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems"

Connected through hyperedge HE-4 (induced by source paper "trust your model iterative label improvement and robust training by confidence based filtering and dataset partitioning"):
  with members:
    - "deep semantic segmentation of natural and medical images a review"
    - "self learning to filter noisy labels with self ensembling"
    - "training object detectors with noisy data"
    - "robust learning under label noise with iterative noise filtering"
    - "an overview of deep learning in medical imaging focusing on mri"
    - "learning to reweight examples for robust deep learning"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-5 (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"
    - "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"
    - "conditional affordance learning for driving in urban environments"

Connected through hyperedge HE-6 (induced by source paper "nuscenes a multimodal dataset for autonomous driving"):
  with members:
    - "how much real data do we actually need analyzing object detection performance using synthetic and real data"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "starnet targeted computation for object detection in point clouds"
    - "spatially aware graph neural networks for relational behavior forecasting from sensor data"
    - "d 2 city a large scale dashcam video dataset of diverse traffic scenarios"
    - "disentangling monocular 3d object detection"
    - "a baseline for 3d multi object tracking"
    - "physics based rendering for improving robustness to rain"

Connected through hyperedge HE-7 (induced by source paper "driving datasets literature review"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "blvd building a large scale 5d semantics benchmark for autonomous driving"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "object detection in 20 years a survey"
    - "the streetlearn environment and dataset"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"

Connected through hyperedge HE-8 (induced by source paper "on single source robustness in deep fusion models"):
  with members:
    - "robust deep multi modal learning based on gated information fusion network"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "state of the art speech recognition with sequence to sequence models"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "mask r cnn"
    - "choosing smartly adaptive multimodal fusion for object detection in changing environments"
    - "ssd single shot multibox detector"
    - "a deep metric for multimodal registration"

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)