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: "dense recurrent neural networks for scene labeling"
  Abstract: Recently recurrent neural networks (RNNs) have demonstrated the ability to improve scene labeling through capturing long-range dependencies among image units. In this paper, we propose dense RNNs for scene labeling by exploring various long-range semantic dependencies among image units. In comparison with existing RNN based approaches, our dense RNNs are able to capture richer contextual dependencies for each image unit via dense connections between each pair of image units, which significantly enhances their discriminative power. Besides, to select relevant and meanwhile restrain irrelevant dependencies for each unit from dense connections, we introduce an attention model into dense RNNs. The attention model enables automatically assigning more importance to helpful dependencies while less weight to unconcerned dependencies. Integrating with convolutional neural networks (CNNs), our method achieves state-of-the-art performances on the PASCAL Context, MIT ADE20K and SiftFlow benchmarks.

Connected through hyperedge HE-1 (induced by source paper "video scene parsing with predictive feature learning"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "laplacian reconstruction and refinement for semantic segmentation"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "approximate policy iteration for budgeted semantic video segmentation"
    - "optical flow with semantic segmentation and localized layers"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "joint optical flow and temporally consistent semantic segmentation"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "scene labeling using gated recurrent units with explicit long range conditioning"):
  with members:
    - "semantic object parsing with graph lstm"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "multi level contextual rnns with attention model for scene labeling"
    - "learning common and specific features for rgb d semantic segmentation with deconvolutional networks"
    - "multi path feedback recurrent neural network for scene parsing"
    - "combining the best of convolutional layers and recurrent layers a hybrid network for semantic segmentation"
    - "pixel recurrent neural networks"
    - "semantic image segmentation via deep parsing network"

Connected through hyperedge HE-3 (induced by source paper "scene parsing with integration of parametric and non parametric models"):
  with members:
    - "conditional random fields as recurrent neural networks"
    - "video tracking using learned hierarchical features"
    - "image parsing with a wide range of classes and scene level context"
    - "exemplar based deep discriminative and shareable feature learning for scene image classification"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

Connected through hyperedge HE-4 (induced by source paper "multi path feedback recurrent neural network for scene parsing"):
  with members:
    - "collaborative layer wise discriminative learning in deep neural networks"
    - "parsenet looking wider to see better"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "feedforward semantic segmentation with zoom out features"

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)