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: "rgb d scene labeling with long short term memorized fusion model"
  Abstract: Semantic labeling of RGB-D scenes is crucial to many intelligent applications including perceptual robotics. It generates pixelwise and fine-grained label maps from simultaneously sensed photometric (RGB) and depth channels. This paper addresses this problem by i) developing a novel Long Short-Term Memorized Fusion (LSTM-F) Model that captures and fuses contextual information from multiple channels of photometric and depth data, and ii) incorporating this model into deep convolutional neural networks (CNNs) for end-to-end training. Specifically, global contexts in photometric and depth channels are, respectively, captured by stacking several convolutional layers and a long short-term memory layer; the memory layer encodes both short-range and long-range spatial dependencies in an image along the vertical direction. Another long short-term memorized fusion layer is set up to integrate the contexts along the vertical direction from different channels, and perform bi-directional propagation of the fused vertical contexts along the horizontal direction to obtain true 2D global contexts. At last, the fused contextual representation is concatenated with the convolutional features extracted from the photometric channels in order to improve the accuracy of fine-scale semantic labeling. Our proposed model has set a new state of the art, i.e., 48.1% average class accuracy over 37 categories 11.8% improvement), on the large-scale SUNRGBD dataset.1

Connected through hyperedge HE-1 (induced by source paper "waterfall atrous spatial pooling architecture for efficient semantic segmentation"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "deformable convolutional networks"
    - "squeeze and excitation networks"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "a review on deep learning techniques applied to semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "semantic segmentation of video sequences with convolutional lstms"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "semantic object parsing with graph lstm"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "aggregated residual transformations for deep neural networks"
    - "end to end instance segmentation and counting with recurrent attention"
    - "virtual worlds as proxy for multi object tracking analysis"

Connected through hyperedge HE-3 (induced by source paper "3d geometry aware semantic labeling of outdoor street scenes"):
  with members:
    - "self supervised learning for stereo matching with self improving ability"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "semantic scene completion from a single depth image"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "beyond skip connections top down modulation for object detection"
    - "semantically guided depth upsampling"
    - "wider or deeper revisiting the resnet model for visual recognition"

Connected through hyperedge HE-4 (induced by source paper "a review on deep learning techniques applied to semantic segmentation"):
  with members:
    - "the 2017 davis challenge on video object segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deep contrast learning for salient object detection"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pruning convolutional neural networks for resource efficient inference"

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)