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: "anytime neural networks via joint optimization of auxiliary losses"
  Abstract: We address the problem of anytime prediction in neural networks. An anytime predictor automatically adjusts to and utilizes available test-time budget: it produces a crude initial result quickly and continuously refines the result afterwards. Traditional feed-forward networks achieve state-of-the-art performance on many machine learning tasks, but cannot produce anytime predictions during their typically expensive computation. In this work, we propose to add auxiliary predictions in a residual network to generate anytime predictions, and optimize these predictions simultaneously. We solve this multi-objective optimization by minimizing a carefully constructed weighted sum of losses. We also oscillate weightings of the losses in each iteration to avoid spurious solutions that are optimal for the sum but not for each individual loss. The proposed approach produces competitive results if computation is interrupted early, and the same level of performance as the original network once computation is finished. Observing that the relative performance gap between the optimal and our proposed anytime network shrinks as the network is near completion, we propose a method to combine anytime networks to achieve more accurate anytime predictions with a constant fraction of additional cost. We evaluate the proposed methods on real-world visual recognition data-sets to demonstrate their anytime performance.

Connected through hyperedge HE-1 (induced by source paper "triple wins boosting accuracy robustness and efficiency together by enabling input adaptive inference"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "certified adversarial robustness via randomized smoothing"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarially trained model compression when robustness meets efficiency"
    - "defensive quantization when efficiency meets robustness"
    - "towards understanding adversarial examples systematically exploring data size task and model factors"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "parity models a general framework for coding based resilience in ml inference"):
  with members:
    - "collage inference tolerating stragglers in distributed neural network inference using coding"
    - "pretzel opening the black box of machine learning prediction serving systems"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning a code machine learning for approximate non linear coded computation"
    - "leveraging coding techniques for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "rafiki machine learning as an analytics service system"
    - "tvm an automated end to end optimizing compiler for deep learning"

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 "log densenet how to sparsify a densenet"):
  with members:
    - "dual path networks"
    - "deep networks with stochastic depth"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "deep roots improving cnn efficiency with hierarchical filter groups"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"

Connected through hyperedge HE-5 (induced by source paper "energy efficient amortized inference with cascaded deep classifiers"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "a downsampled variant of imagenet as an alternative to the cifar datasets"
    - "adaptive computation time for recurrent neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "wide residual networks"

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.LG(Machine Learning)