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: "rednet residual encoder decoder network for indoor rgb d semantic segmentation"
  Abstract: Indoor semantic segmentation has always been a difficult task in computer vision. In this paper, we propose an RGB-D residual encoder-decoder architecture, named RedNet, for indoor RGB-D semantic segmentation. In RedNet, the residual module is applied to both the encoder and decoder as the basic building block, and the skip-connection is used to bypass the spatial feature between the encoder and decoder. In order to incorporate the depth information of the scene, a fusion structure is constructed, which makes inference on RGB image and depth image separately, and fuses their features over several layers. In order to efficiently optimize the network's parameters, we propose a `pyramid supervision' training scheme, which applies supervised learning over different layers in the decoder, to cope with the problem of gradients vanishing. Experiment results show that the proposed RedNet(ResNet-50) achieves a state-of-the-art mIoU accuracy of 47.8% on the SUN RGB-D benchmark dataset.

Connected through hyperedge HE-1 (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"
    - "squeeze and excitation networks"

Connected through hyperedge HE-2 (induced by source paper "quadtree generating networks efficient hierarchical scene parsing with sparse convolutions"):
  with members:
    - "anytime neural prediction via slicing networks vertically"
    - "ccnet criss cross attention for semantic segmentation"
    - "sbnet sparse blocks network for fast inference"
    - "unified perceptual parsing for scene understanding"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "dual attention network for scene segmentation"
    - "context encoding for semantic segmentation"
    - "learning anytime predictions in neural networks via adaptive loss balancing"

Connected through hyperedge HE-3 (induced by source paper "learning your way without map or compass panoramic target driven visual navigation"):
  with members:
    - "long range indoor navigation with prm rl"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "habitat a platform for embodied ai research"
    - "learning navigation behaviors end to end with autorl"
    - "gibson env real world perception for embodied agents"
    - "on evaluation of embodied navigation agents"
    - "learning to navigate in cities without a map"
    - "minos multimodal indoor simulator for navigation in complex environments"

Connected through hyperedge HE-4 (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"

Connected through hyperedge HE-5 (induced by source paper "variation network learning high level attributes for controlled input manipulation"):
  with members:
    - "learning discrete and continuous factors of data via alternating disentanglement"
    - "lipschitz generative adversarial nets"
    - "diagnosing and enhancing vae models"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "deep learning based decoding for constrained sequence codes"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "sampling generative networks"
    - "glsr vae geodesic latent space regularization for variational autoencoder architectures"

Connected through hyperedge HE-6 (induced by source paper "small obstacle avoidance based on rgb d semantic segmentation"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "mergenet a deep net architecture for small obstacle discovery"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "incorporating depth into both cnn and crf for indoor semantic segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-7 (induced by source paper "residual encoder decoder network for deep subspace clustering"):
  with members:
    - "sparse dense subspace clustering"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "deep subspace clustering networks"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "training very deep networks"

Connected through hyperedge HE-8 (induced by source paper "deep learning based wearable assistive system for visually impaired people"):
  with members:
    - "deepvio self supervised deep learning of monocular visual inertial odometry using 3d geometric constraints"
    - "wearable travel aid for environment perception and navigation of visually impaired people"
    - "a unified framework for mutual improvement of slam and semantic segmentation"
    - "deep global relative networks for end to end 6 dof visual localization and odometry"
    - "learning transferable uav for forest visual perception"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "smart guiding glasses for visually impaired people in indoor environment"
    - "the cityscapes dataset for semantic urban scene understanding"

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)