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: "incorporating depth into both cnn and crf for indoor semantic segmentation"
  Abstract: To improve segmentation performance, a novel neural network architecture (termed DFCN-DCRF) is proposed, which combines an RGB-D fully convolutional neural network (DFCN) with a depth-sensitive fully-connected conditional random field (DCRF). First, a DFCN architecture which fuses depth information into the early layers and applies dilated convolution for later contextual reasoning is designed. Then, a depth-sensitive fully-connected conditional random field (DCRF) is proposed and combined with the previous DFCN to refine the preliminary result. Comparative experiments show that the proposed DFCN-DCRF has the best performance compared with most state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "elkppnet an edge aware neural network with large kernel pyramid pooling for learning discriminative features in semantic segmentation"):
  with members:
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "semantic edge detection with diverse deep supervision"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning"
    - "focal loss for dense object detection"
    - "richer convolutional features for edge detection"
    - "on support relations and semantic scene graphs"

Connected through hyperedge HE-2 (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"
    - "rednet residual encoder decoder network for indoor rgb d 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-3 (induced by source paper "learning common representation from rgb and depth images"):
  with members:
    - "deep depth completion of a single rgb d image"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "multimodal deep learning for robust rgb d object recognition"

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)