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: "surfconv bridging 3d and 2d convolution for rgbd images"
  Abstract: We tackle the problem of using 3D information in convolutional neural networks for down-stream recognition tasks. Using depth as an additional channel alongside the RGB input has the scale variance problem present in image convolution based approaches. On the other hand, 3D convolution wastes a large amount of memory on mostly unoccupied 3D space, which consists of only the surface visible to the sensor. Instead, we propose SurfConv, which "slides" compact 2D filters along the visible 3D surface. SurfConv is formulated as a simple depth-aware multi-scale 2D convolution, through a new Data-Driven Depth Discretization (D4) scheme. We demonstrate the effectiveness of our method on indoor and outdoor 3D semantic segmentation datasets. Our method achieves state-of-the-art performance with less than 30% parameters used by the 3D convolution-based approaches.

Connected through hyperedge HE-1 (induced by source paper "leveraging shape completion for 3d siamese tracking"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "learning depth with convolutional spatial propagation network"
    - "real time mdnet"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "segstereo exploiting semantic information for disparity estimation"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "learning 3d shape completion under weak supervision"

Connected through hyperedge HE-2 (induced by source paper "learning propagation for arbitrarily structured data"):
  with members:
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "a simple baseline algorithm for graph classification"
    - "splatnet sparse lattice networks for point cloud processing"
    - "fast end to end trainable guided filter"
    - "videos as space time region graphs"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "tangent convolutions for dense prediction in 3d"
    - "non local neural networks"

Connected through hyperedge HE-3 (induced by source paper "3d neighborhood convolution learning depth aware features for rgb d and rgb semantic segmentation"):
  with members:
    - "learning a discriminative feature network for semantic segmentation"
    - "deep ordinal regression network for monocular depth estimation"
    - "depth aware cnn for rgb d segmentation"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "deformable convnets v2 more deformable better results"
    - "non local neural networks"
    - "deformable convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "m3d rpn monocular 3d region proposal network for object detection"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "a general pipeline for 3d detection of vehicles"
    - "deep feature pyramid reconfiguration for object detection"
    - "pedestrian detection with autoregressive network phases"
    - "localization recall precision lrp a new performance metric for object detection"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
    - "illuminating pedestrians via simultaneous detection segmentation"

Connected through hyperedge HE-5 (induced by source paper "deep surface normal estimation with hierarchical rgb d fusion"):
  with members:
    - "depth aware cnn for rgb d segmentation"
    - "deep depth completion of a single rgb d image"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "splatnet sparse lattice networks for point cloud processing"
    - "3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model"
    - "noise2noise learning image restoration without clean data"
    - "deep graph laplacian regularization for robust denoising of real images"
    - "deformable shape completion with graph convolutional autoencoders"

Connected through hyperedge HE-6 (induced by source paper "pointconv deep convolutional networks on 3d point clouds"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "flex convolution million scale point cloud learning beyond grid worlds"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "dynamic graph cnn for learning on point clouds"
    - "tangent convolutions for dense prediction in 3d"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

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)