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: "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
  Abstract: We present 3DMV, a novel method for 3D semantic scene segmentation of RGB-D scans in indoor environments using a joint 3D-multi-view prediction network. In contrast to existing methods that either use geometry or RGB data as input for this task, we combine both data modalities in a joint, end-to-end network architecture. Rather than simply projecting color data into a volumetric grid and operating solely in 3D -- which would result in insufficient detail -- we first extract feature maps from associated RGB images. These features are then mapped into the volumetric feature grid of a 3D network using a differentiable backprojection layer. Since our target is 3D scanning scenarios with possibly many frames, we use a multi-view pooling approach in order to handle a varying number of RGB input views. This learned combination of RGB and geometric features with our joint 2D-3D architecture achieves significantly better results than existing baselines. For instance, our final result on the ScanNet 3D segmentation benchmark increases from 52.8\% to 75\% accuracy compared to existing volumetric architectures.

Connected through hyperedge HE-1 (induced by source paper "robust 6d object pose estimation by learning rgb d features"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "dpod 6d pose object detector and refiner"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "explaining the ambiguity of object detection and 6d pose from visual data"
    - "bop benchmark for 6d object pose estimation"
    - "deep object pose estimation for semantic robotic grasping of household objects"

Connected through hyperedge HE-2 (induced by source paper "learning and memorizing representative prototypes for 3d point cloud semantic and instance segmentation"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "3d bird s eye view instance segmentation"
    - "kpconv flexible and deformable convolution for point clouds"
    - "associatively segmenting instances and semantics in point clouds"
    - "large scale long tailed recognition in an open world"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "3d instance segmentation via multi task metric learning"
    - "deep hough voting for 3d object detection in point clouds"

Connected through hyperedge HE-3 (induced by source paper "comparing view based and map based semantic labelling in real time slam"):
  with members:
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "volumetric instance aware semantic mapping and 3d object discovery"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "orthographic feature transform for monocular 3d object detection"
    - "octnetfusion learning depth fusion from data"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "da rnn semantic mapping with data associated recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "sceneencoder scene aware semantic segmentation of point clouds with a learnable scene descriptor"):
  with members:
    - "octree guided cnn with spherical kernels for 3d point clouds"
    - "hierarchical point edge interaction network for point cloud semantic segmentation"
    - "shellnet efficient point cloud convolutional neural networks using concentric shells statistics"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "so net self organizing network for point cloud analysis"
    - "pointconv deep convolutional networks on 3d point clouds"

Connected through hyperedge HE-5 (induced by source paper "a review on deep learning techniques for 3d sensed data classification"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "context prediction for unsupervised deep learning on point clouds"
    - "kpconv flexible and deformable convolution for point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "splatnet sparse lattice networks for point cloud processing"
    - "so net self organizing network for point cloud analysis"
    - "adversarial autoencoders for compact representations of 3d point clouds"

Connected through hyperedge HE-6 (induced by source paper "self supervised model adaptation for multimodal semantic segmentation"):
  with members:
    - "robot localization in floor plans using a room layout edge extraction network"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "in defense of classical image processing fast depth completion on the cpu"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-7 (induced by source paper "3d instance segmentation via multi task metric learning"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "pixor real time 3d object detection from point clouds"
    - "3d graph embedding learning with a structure aware loss function for point cloud semantic instance segmentation"
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "3d sis 3d semantic instance segmentation of rgb d scans"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "semi convolutional operators for instance segmentation"

Connected through hyperedge HE-8 (induced by source paper "ddsl deep differentiable simplex layer for learning geometric signals"):
  with members:
    - "spherical cnns on unstructured grids"
    - "convolutional neural networks on non uniform geometrical signals using euclidean spectral transformation"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "learning category specific mesh reconstruction from image collections"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "unsupervised training for 3d morphable model regression"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

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)