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: "deepicp an end to end deep neural network for 3d point cloud registration"
  Abstract: We present DeepICP - a novel end-to-end learning-based 3D point cloud registration framework that achieves comparable registration accuracy to prior state-of-the-art geometric methods. Different from other keypoint based methods where a RANSAC procedure is usually needed, we implement the use of various deep neural network structures to establish an end-to-end trainable network. Our keypoint detector is trained through this end-to-end structure and enables the system to avoid the inference of dynamic objects, leverages the help of sufficiently salient features on stationary objects, and as a result, achieves high robustness. Rather than searching the corresponding points among existing points, the key contribution is that we innovatively generate them based on learned matching probabilities among a group of candidates, which can boost the registration accuracy. Our loss function incorporates both the local similarity and the global geometric constraints to ensure all above network designs can converge towards the right direction. We comprehensively validate the effectiveness of our approach using both the KITTI dataset and the Apollo-SouthBay dataset. Results demonstrate that our method achieves comparable or better performance than the state-of-the-art geometry-based methods. Detailed ablation and visualization analysis are included to further illustrate the behavior and insights of our network. The low registration error and high robustness of our method makes it attractive for substantial applications relying on the point cloud registration task.

Connected through hyperedge HE-1 (induced by source paper "a rotation invariant framework for deep point cloud analysis"):
  with members:
    - "total denoising unsupervised learning of 3d point cloud cleaning"
    - "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"
    - "kpconv flexible and deformable convolution for point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "pu gan a point cloud upsampling adversarial network"
    - "rotation invariant convolutions for 3d point clouds deep learning"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "unsupervised multi task feature learning on point clouds"

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

Connected through hyperedge HE-3 (induced by source paper "stickypillars robust feature matching on point clouds using graph neural networks"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "lo net deep real time lidar odometry"
    - "superglue learning feature matching with graph neural networks"
    - "loam_livox a fast robust high precision lidar odometry and mapping package for lidars of small fov"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "lf net learning local features from images"

Connected through hyperedge HE-4 (induced by source paper "deep closest point learning representations for point cloud registration"):
  with members:
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "open3d a modern library for 3d data processing"
    - "relational deep reinforcement learning"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-5 (induced by source paper "one framework to register them all pointnet encoding for point cloud alignment"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "3dregnet a deep neural network for 3d point registration"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "deep closest point learning representations for point cloud registration"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "flownet3d learning scene flow in 3d point clouds"

Connected through hyperedge HE-6 (induced by source paper "tabulated mlp for fast point feature embedding"):
  with members:
    - "kpconv flexible and deformable convolution for point clouds"
    - "pcrnet point cloud registration network using pointnet encoding"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "deep closest point learning representations for point cloud registration"
    - "prnet self supervised learning for partial to partial registration"
    - "pointpillars fast encoders for object detection from point clouds"
    - "splatnet sparse lattice networks for point cloud processing"
    - "eventnet asynchronous recursive event processing"

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)