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: "3d ssd learning hierarchical features from rgb d images for amodal 3d object detection"
  Abstract: This paper aims at developing a faster and a more accurate solution to the amodal 3D object detection problem for indoor scenes. It is achieved through a novel neural network that takes a pair of RGB-D images as the input and delivers oriented 3D bounding boxes as the output. The network, named 3D-SSD, composed of two parts: hierarchical feature fusion and multi-layer prediction. The hierarchical feature fusion combines appearance and geometric features from RGB-D images while the multi-layer prediction utilizes multi-scale features for object detection. As a result, the network can exploit 2.5D representations in a synergetic way to improve the accuracy and efficiency. The issue of object sizes is addressed by attaching a set of 3D anchor boxes with varying sizes to every location of the prediction layers. At the end stage, the category scores for 3D anchor boxes are generated with adjusted positions, sizes and orientations respectively, leading to the final detections using non-maximum suppression. In the training phase, the positive samples are identified with the aid of 2D ground truth to avoid the noisy estimation of depth from raw data, which guide to a better converged model. Experiments performed on the challenging SUN RGB-D dataset show that our algorithm outperforms the state-of-the-art Deep Sliding Shape by 10.2% mAP and 88x faster. Further, experiments also suggest our approach achieves comparable accuracy and is 386x faster than the state-of-art method on the NYUv2 dataset even with a smaller input image size.

Connected through hyperedge HE-1 (induced by source paper "deepgoal learning to drive with driving intention from human control demonstration"):
  with members:
    - "yolov3 an incremental improvement"
    - "conditional affordance learning for driving in urban environments"
    - "variational end to end navigation and localization"
    - "laser map aided visual inertial localization in changing environment"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "end to end driving via conditional imitation learning"
    - "learning from maps visual common sense for autonomous driving"

Connected through hyperedge HE-2 (induced by source paper "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "ipod intensive point based object detector for point cloud"
    - "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"
    - "focal loss for dense object detection"
    - "frustum pointnets for 3d object detection from rgb d data"

Connected through hyperedge HE-3 (induced by source paper "towards navigation without precise localization weakly supervised learning of goal directed navigation cost map"):
  with members:
    - "yolov3 an incremental improvement"
    - "variational end to end navigation and localization"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "laser map aided visual inertial localization in changing environment"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "ground edge based lidar localization without a reflectivity calibration for autonomous driving"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "robust and precise vehicle localization based on multi sensor fusion in diverse city scenes"

Connected through hyperedge HE-4 (induced by source paper "spatiotemporal decoupling based lidar camera calibration under arbitrary configurations"):
  with members:
    - "lidar and camera calibration using motion estimated by sensor fusion odometry"
    - "laser map aided visual inertial localization in changing environment"
    - "reflectance intensity assisted automatic and accurate extrinsic calibration of 3d lidar and panoramic camera using a printed chessboard"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

Connected through hyperedge HE-5 (induced by source paper "lidar camera calibration under arbitrary configurations observability and methods"):
  with members:
    - "laser map aided visual inertial localization in changing environment"
    - "reflectance intensity assisted automatic and accurate extrinsic calibration of 3d lidar and panoramic camera using a printed chessboard"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

Connected through hyperedge HE-6 (induced by source paper "group attention single shot detector ga ssd finding pulmonary nodules in large scale ct images"):
  with members:
    - "multi attention multi class constraint for fine grained image recognition"
    - "deep learning for generic object detection a survey"
    - "non local neural networks"
    - "attention is all you need"
    - "relation networks for object detection"
    - "dssd deconvolutional single shot detector"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "aggregated residual transformations for deep neural networks"

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)