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: "learning dynamic hierarchical models for anytime scene labeling"
  Abstract: With increasing demand for efficient image and video analysis, test-time cost of scene parsing becomes critical for many large-scale or time-sensitive vision applications. We propose a dynamic hierarchical model for anytime scene labeling that allows us to achieve flexible trade-offs between efficiency and accuracy in pixel-level prediction. In particular, our approach incorporates the cost of feature computation and model inference, and optimizes the model performance for any given test-time budget by learning a sequence of image-adaptive hierarchical models. We formulate this anytime representation learning as a Markov Decision Process with a discrete-continuous state-action space. A high-quality policy of feature and model selection is learned based on an approximate policy iteration method with action proposal mechanism. We demonstrate the advantages of our dynamic non-myopic anytime scene parsing on three semantic segmentation datasets, which achieves $90\%$ of the state-of-the-art performances by using $15\%$ of their overall costs.

Connected through hyperedge HE-1 (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"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "crafting a toolchain for image restoration by deep reinforcement learning"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "merge or not learning to group faces via imitation learning"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "attention aware face hallucination via deep reinforcement learning"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "memnet a persistent memory network for image restoration"

Connected through hyperedge HE-3 (induced by source paper "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"):
  with members:
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "fashion landmark detection in the wild"
    - "deep learning markov random field for semantic segmentation"
    - "training region based object detectors with online hard example mining"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "temporal segment networks towards good practices for deep action recognition"
    - "semantic image segmentation via deep parsing network"

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)