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: "learning common and specific features for rgb d semantic segmentation with deconvolutional networks"
  Abstract: In this paper, we tackle the problem of RGB-D semantic segmentation of indoor images. We take advantage of deconvolutional networks which can predict pixel-wise class labels, and develop a new structure for deconvolution of multiple modalities. We propose a novel feature transformation network to bridge the convolutional networks and deconvolutional networks. In the feature transformation network, we correlate the two modalities by discovering common features between them, as well as characterize each modality by discovering modality specific features. With the common features, we not only closely correlate the two modalities, but also allow them to borrow features from each other to enhance the representation of shared information. With specific features, we capture the visual patterns that are only visible in one modality. The proposed network achieves competitive segmentation accuracy on NYU depth dataset V1 and V2.

Connected through hyperedge HE-1 (induced by source paper "modality compensation network cross modal adaptation for action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "temporal bilinear networks for video action recognition"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "pku mmd a large scale benchmark for continuous multi modal human action understanding"

Connected through hyperedge HE-2 (induced by source paper "3d gated recurrent fusion for semantic scene completion"):
  with members:
    - "rgbd based dimensional decomposition residual network for 3d semantic scene completion"
    - "adaptive fusion for rgb d salient object detection"
    - "efficient semantic scene completion network with spatial group convolution"
    - "scalable place recognition under appearance change for autonomous driving"
    - "two stream 3d semantic scene completion"
    - "unsupervised rgbd video object segmentation using gans"
    - "rgb d based action recognition with light weight 3d convolutional networks"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"

Connected through hyperedge HE-3 (induced by source paper "scene completenesss aware lidar depth completion for driving scenario"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "sdnet semantically guided depth estimation network"
    - "depth coefficients for depth completion"
    - "depth completion from sparse lidar data with depth normal constraints"
    - "3d lidar and stereo fusion using stereo matching network with conditional cost volume normalization"
    - "deep rgb d canonical correlation analysis for sparse depth completion"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "learning depth with convolutional spatial propagation network"

Connected through hyperedge HE-4 (induced by source paper "hard pixel mining for depth privileged semantic segmentation"):
  with members:
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "pattern affinitive propagation across depth surface normal and semantic segmentation"
    - "dada depth aware domain adaptation in semantic segmentation"
    - "deep learning under privileged information using heteroscedastic dropout"
    - "depth aware cnn for rgb d segmentation"
    - "depth adaptive deep neural network for semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"
    - "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"

Connected through hyperedge HE-5 (induced by source paper "multi modal attention based fusion model for semantic segmentation of rgb depth images"):
  with members:
    - "depth aware cnn for rgb d segmentation"
    - "depth adaptive deep neural network for semantic segmentation"
    - "pixel wise attentional gating for parsimonious pixel labeling"
    - "cbam convolutional block attention module"
    - "squeeze and excitation networks"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-6 (induced by source paper "sensor fusion for joint 3d object detection and semantic segmentation"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "a general pipeline for 3d detection of vehicles"
    - "pointpillars fast encoders for object detection from point clouds"
    - "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"
    - "deep layer aggregation"

Connected through hyperedge HE-7 (induced by source paper "rgbd based dimensional decomposition residual network for 3d semantic scene completion"):
  with members:
    - "learning pairwise relationship for multi object detection in crowded scenes"
    - "efficient semantic scene completion network with spatial group convolution"
    - "semantic scene completion combining colour and depth preliminary experiments"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "two stream 3d semantic scene completion"
    - "view volume network for semantic scene completion from a single depth image"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-8 (induced by source paper "rfbnet deep multimodal networks with residual fusion blocks for rgb d semantic segmentation"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "gff gated fully fusion for semantic segmentation"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "long term visual localization using semantically segmented images"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "in defense of classical image processing fast depth completion on the cpu"

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)