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: "indoor semantic segmentation using depth information"
  Abstract: This work addresses multi-class segmentation of indoor scenes with RGB-D inputs. While this area of research has gained much attention recently, most works still rely on hand-crafted features. In contrast, we apply a multiscale convolutional network to learn features directly from the images and the depth information. We obtain state-of-the-art on the NYU-v2 depth dataset with an accuracy of 64.5%. We illustrate the labeling of indoor scenes in videos sequences that could be processed in real-time using appropriate hardware such as an FPGA.

Connected through hyperedge HE-1 (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-2 (induced by source paper "real time fusion network for rgb d semantic segmentation incorporating unexpected obstacle detection for road driving images"):
  with members:
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "see clearer at night towards robust nighttime semantic segmentation through day night image conversion"
    - "rfbnet deep multimodal networks with residual fusion blocks for rgb d semantic segmentation"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "mergenet a deep net architecture for small obstacle discovery"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"

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 "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"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "so net self organizing network for point cloud analysis"

Connected through hyperedge HE-5 (induced by source paper "adversarial structure matching for structured prediction tasks"):
  with members:
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "taskonomy disentangling task transfer learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "regularizing deep networks by modeling and predicting label structure"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "deep value networks learn to evaluate and iteratively refine structured outputs"
    - "interpreting deep visual representations via network dissection"
    - "learning affinity via spatial propagation networks"

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"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "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"

Connected through hyperedge HE-7 (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-8 (induced by source paper "normal assisted stereo depth estimation"):
  with members:
    - "self supervised learning for single view depth and surface normal estimation"
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "dpsnet end to end deep plane sweep stereo"
    - "ga net guided aggregation net for end to end stereo matching"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep depth completion of a single rgb d image"

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)