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: "impatient dnns deep neural networks with dynamic time budgets"
  Abstract: We propose Impatient Deep Neural Networks (DNNs) which deal with dynamic time budgets during application. They allow for individual budgets given a priori for each test example and for anytime prediction, i.e., a possible interruption at multiple stages during inference while still providing output estimates. Our approach can therefore tackle the computational costs and energy demands of DNNs in an adaptive manner, a property essential for real-time applications. Our Impatient DNNs are based on a new general framework of learning dynamic budget predictors using risk minimization, which can be applied to current DNN architectures by adding early prediction and additional loss layers. A key aspect of our method is that all of the intermediate predictors are learned jointly. In experiments, we evaluate our approach for different budget distributions, architectures, and datasets. Our results show a significant gain in expected accuracy compared to common baselines.

Connected through hyperedge HE-1 (induced by source paper "be your own teacher improve the performance of convolutional neural networks via self distillation"):
  with members:
    - "slimmable neural networks"
    - "ktan knowledge transfer adversarial network"
    - "stochastic downsampling for cost adjustable inference and improved regularization in convolutional networks"
    - "meal multi model ensemble via adversarial learning"
    - "label refinery improving imagenet classification through label progression"
    - "skipnet learning dynamic routing in convolutional networks"
    - "focal loss for dense object detection"
    - "deep mutual learning"

Connected through hyperedge HE-2 (induced by source paper "residual pyramid learning for single shot semantic segmentation"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "fast and accurate image super resolution with deep laplacian pyramid networks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-3 (induced by source paper "slimmable neural networks"):
  with members:
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "ai benchmark running deep neural networks on android smartphones"
    - "efficient video object segmentation via network modulation"
    - "stochastic downsampling for cost adjustable inference and improved regularization in convolutional networks"
    - "mnasnet platform aware neural architecture search for mobile"
    - "skipnet learning dynamic routing in convolutional networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"

Connected through hyperedge HE-4 (induced by source paper "imagenet pre trained models with batch normalization"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "dynamic memory networks for visual and textual question answering"
    - "modeling context in referring expressions"
    - "generation and comprehension of unambiguous object descriptions"
    - "vqa visual question answering"
    - "convolutional patch networks with spatial prior for road detection and 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)