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: "semantic 3d occupancy mapping through efficient high order crfs"
  Abstract: Semantic 3D mapping can be used for many applications such as robot navigation and virtual interaction. In recent years, there has been great progress in semantic segmentation and geometric 3D mapping. However, it is still challenging to combine these two tasks for accurate and large-scale semantic mapping from images. In the paper, we propose an incremental and (near) real-time semantic mapping system. A 3D scrolling occupancy grid map is built to represent the world, which is memory and computationally efficient and bounded for large scale environments. We utilize the CNN segmentation as prior prediction and further optimize 3D grid labels through a novel CRF model. Superpixels are utilized to enforce smoothness and form robust P N high order potential. An efficient mean field inference is developed for the graph optimization. We evaluate our system on the KITTI dataset and improve the segmentation accuracy by 10% over existing systems.

Connected through hyperedge HE-1 (induced by source paper "occuseg occupancy aware 3d instance segmentation"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "3d graph embedding learning with a structure aware loss function for point cloud semantic instance segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "associatively segmenting instances and semantics in point clouds"
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "3d instance segmentation via multi task metric learning"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"

Connected through hyperedge HE-2 (induced by source paper "monocular semantic occupancy grid mapping with convolutional variational encoder decoder networks"):
  with members:
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "semantic visual localization"
    - "unsupervised learning of depth and ego motion from video"
    - "cnn slam real time dense monocular slam with learned depth prediction"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "unsupervised monocular depth estimation with left right consistency"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-3 (induced by source paper "jsis3d joint semantic instance segmentation of 3d point clouds with multi task pointwise networks and multi value conditional random fields"):
  with members:
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "dynamic graph cnn for learning on point clouds"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "segcloud semantic segmentation of 3d point clouds"

Connected through hyperedge HE-4 (induced by source paper "panopticfusion online volumetric semantic mapping at the level of stuff and things"):
  with members:
    - "masc multi scale affinity with sparse convolution for 3d instance segmentation"
    - "fusion volumetric object level slam"
    - "3d sis 3d semantic instance segmentation of rgb d scans"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "splatnet sparse lattice networks for point cloud processing"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "gspn generative shape proposal network for 3d instance segmentation in point cloud"
    - "tangent convolutions for dense prediction in 3d"

Connected through hyperedge HE-5 (induced by source paper "incremental class discovery for semantic segmentation with rgbd sensing"):
  with members:
    - "fast and accurate semantic mapping through geometric based incremental segmentation"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
    - "segcloud semantic segmentation of 3d point clouds"
    - "pointwise convolutional neural networks"
    - "w net a deep model for fully unsupervised image segmentation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "environment reconstruction on depth images using generative adversarial networks"):
  with members:
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "semantic road layout understanding by generative adversarial inpainting"
    - "deep depth completion of a single rgb d image"
    - "generative image inpainting with contextual attention"
    - "improved training of wasserstein gans"
    - "depth image inpainting improving low rank matrix completion with low gradient regularization"
    - "sparsity invariant cnns"

Connected through hyperedge HE-7 (induced by source paper "bayesian spatial kernel smoothing for scalabledense semantic mapping"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "sampling based incremental information gathering with applications to robotic exploration and environmental monitoring"
    - "rtseg real time semantic segmentation comparative study"
    - "contact aided invariant extended kalman filtering for legged robot state estimation"
    - "gaussian processes semantic map representation"
    - "sparse bayesian inference for dense semantic mapping"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "probe gk predictive robust estimation using generalized kernels"

Connected through hyperedge HE-8 (induced by source paper "real time progressive 3d semantic segmentation for indoor scene"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "dynamic graph cnn for learning on point clouds"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "deep projective 3d semantic segmentation"
    - "real time semantic image segmentation via spatial sparsity"
    - "large scale point cloud semantic segmentation with superpoint graphs"

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)