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: "frustum pointnets for 3d object detection from rgb d data"
  Abstract: In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes. While previous methods focus on images or 3D voxels, often obscuring natural 3D patterns and invariances of 3D data, we directly operate on raw point clouds by popping up RGB-D scans. However, a key challenge of this approach is how to efficiently localize objects in point clouds of large-scale scenes (region proposal). Instead of solely relying on 3D proposals, our method leverages both mature 2D object detectors and advanced 3D deep learning for object localization, achieving efficiency as well as high recall for even small objects. Benefited from learning directly in raw point clouds, our method is also able to precisely estimate 3D bounding boxes even under strong occlusion or with very sparse points. Evaluated on KITTI and SUN RGB-D 3D detection benchmarks, our method outperforms the state of the art by remarkable margins while having real-time capability.

Connected through hyperedge HE-1 (induced by source paper "review deep learning on 3d point clouds"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "shape2motion joint analysis of motion parts and attributes from 3d shapes"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "blvd building a large scale 5d semantics benchmark for autonomous driving"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "pixor real time 3d object detection from point clouds"
    - "densepoint learning densely contextual representation for efficient point cloud processing"

Connected through hyperedge HE-2 (induced by source paper "dsgn deep stereo geometry network for 3d object detection"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "depth coefficients for depth completion"
    - "disentangling monocular 3d object detection"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "hierarchical deep stereo matching on high resolution images"
    - "dpsnet end to end deep plane sweep stereo"

Connected through hyperedge HE-3 (induced by source paper "3d point cloud processing and learning for autonomous driving"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "self driving cars a survey"
    - "governing autonomous vehicles emerging responses for safety liability privacy cybersecurity and industry risks"
    - "pixor real time 3d object detection from point clouds"
    - "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"
    - "3d point cloud denoising via deep neural network based local surface estimation"
    - "fvnet 3d front view proposal generation for real time object detection from point clouds"

Connected through hyperedge HE-4 (induced by source paper "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "pixor real time 3d object detection from point clouds"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "semantic foggy scene understanding with synthetic data"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "robust video content alignment and compensation for clear vision through the rain"
    - "residual dense network for image super resolution"
    - "o haze a dehazing benchmark with real hazy and haze free outdoor images"

Connected through hyperedge HE-5 (induced by source paper "adapting object detectors with conditional domain normalization"):
  with members:
    - "compound domain adaptation in an open world"
    - "precise synthetic image and lidar presil dataset for autonomous vehicle perception"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "domain adaptation for structured output via discriminative patch representations"
    - "semantic image synthesis with spatially adaptive normalization"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "pointdan a multi scale 3d domain adaption network for point cloud representation"
    - "moment matching for multi source domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "deep neural network perception models and robust autonomous driving systems"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "one pixel attack for fooling deep neural networks"
    - "self driving cars a survey"
    - "adversarially robust distillation"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "a survey of autonomous driving common practices and emerging technologies"
    - "panoptic deeplab a simple strong and fast baseline for bottom up panoptic segmentation"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"

Connected through hyperedge HE-7 (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-8 (induced by source paper "imvotenet boosting 3d object detection in point clouds with image votes"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "what makes training multi modal networks hard"
    - "deep hough voting for 3d object detection in point clouds"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "pointpillars fast encoders for object detection from point clouds"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "the sound of pixels"
    - "scan2cad learning cad model alignment in rgb d scans"

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)