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: "depth aware cnn for rgb d segmentation"
  Abstract: Convolutional neural networks (CNN) are limited by the lack of capability to handle geometric information due to the fixed grid kernel structure. The availability of depth data enables progress in RGB-D semantic segmentation with CNNs. State-of-the-art methods either use depth as additional images or process spatial information in 3D volumes or point clouds. These methods suffer from high computation and memory cost. To address these issues, we present Depth-aware CNN by introducing two intuitive, flexible and effective operations: depth-aware convolution and depth-aware average pooling. By leveraging depth similarity between pixels in the process of information propagation, geometry is seamlessly incorporated into CNN. Without introducing any additional parameters, both operators can be easily integrated into existing CNNs. Extensive experiments and ablation studies on challenging RGB-D semantic segmentation benchmarks validate the effectiveness and flexibility of our approach.

Connected through hyperedge HE-1 (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-2 (induced by source paper "geoconv geodesic guided convolution for facial action unit recognition"):
  with members:
    - "semantic relationships guided representation learning for facial action unit recognition"
    - "conditional adversarial synthesis of 3d facial action units"
    - "deep structure inference network for facial action unit recognition"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "facial action unit detection using attention and relation learning"
    - "cnn based real time dense face reconstruction with inverse rendered photo realistic face images"
    - "deformable convolutional networks"
    - "sgdr stochastic gradient descent with restarts"

Connected through hyperedge HE-3 (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 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"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-4 (induced by source paper "mix and match networks multi domain alignment for unpaired image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "multimodal unsupervised image to image translation"
    - "conditional image to image translation"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "image to image translation for cross domain disentanglement"

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 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"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "3d neighborhood convolution learning depth aware features for rgb d and rgb semantic segmentation"):
  with members:
    - "surfconv bridging 3d and 2d convolution for rgbd images"
    - "learning a discriminative feature network for semantic segmentation"
    - "deep ordinal regression network for monocular depth estimation"
    - "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-7 (induced by source paper "cascaded context pyramid for full resolution 3d semantic scene completion"):
  with members:
    - "rgbd based dimensional decomposition residual network for 3d semantic scene completion"
    - "efficient semantic scene completion network with spatial group convolution"
    - "semantic scene completion combining colour and depth preliminary experiments"
    - "agile amulet real time salient object detection with contextual attention"
    - "two stream 3d semantic scene completion"
    - "view volume network for semantic scene completion from a single depth image"
    - "semantic labeling in very high resolution images via a self cascaded convolutional neural network"
    - "amulet aggregating multi level convolutional features for salient object detection"

Connected through hyperedge HE-8 (induced by source paper "deep rgb d canonical correlation analysis for sparse depth completion"):
  with members:
    - "depth coefficients for depth completion"
    - "dfinenet ego motion estimation and depth refinement from sparse noisy depth input with rgb guidance"
    - "dense depth posterior ddp from single image and sparse range"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "learning depth with convolutional spatial propagation network"
    - "deep depth completion of a single rgb d image"
    - "sparse and dense data with cnns depth completion and semantic segmentation"

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)