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: "multi scale dense networks for resource efficient image classification"
  Abstract: In this paper we investigate image classification with computational resource limits at test time. Two such settings are: 1. anytime classification, where the network's prediction for a test example is progressively updated, facilitating the output of a prediction at any time; and 2. budgeted batch classification, where a fixed amount of computation is available to classify a set of examples that can be spent unevenly across "easier" and "harder" inputs. In contrast to most prior work, such as the popular Viola and Jones algorithm, our approach is based on convolutional neural networks. We train multiple classifiers with varying resource demands, which we adaptively apply during test time. To maximally re-use computation between the classifiers, we incorporate them as early-exits into a single deep convolutional neural network and inter-connect them with dense connectivity. To facilitate high quality classification early on, we use a two-dimensional multi-scale network architecture that maintains coarse and fine level features all-throughout the network. Experiments on three image-classification tasks demonstrate that our framework substantially improves the existing state-of-the-art in both settings.

Connected through hyperedge HE-1 (induced by source paper "learning enriched features for real image restoration and enhancement"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "kindling the darkness a practical low light image enhancer"
    - "residual non local attention networks for image restoration"
    - "making convolutional networks shift invariant again"
    - "a deep journey into super resolution a survey"
    - "selective kernel networks"
    - "deep learning for image super resolution a survey"
    - "real image denoising with feature attention"

Connected through hyperedge HE-2 (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-3 (induced by source paper "mutualnet adaptive convnet via mutual learning from network width and resolution"):
  with members:
    - "adascale towards real time video object detection using adaptive scaling"
    - "universally slimmable networks and improved training techniques"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "fast autoaugment"
    - "deep high resolution representation learning for human pose estimation"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-4 (induced by source paper "resolution adaptive networks for efficient inference"):
  with members:
    - "convolutional networks with dense connectivity"
    - "adaptative inference cost with convolutional neural mixture models"
    - "deep high resolution representation learning for human pose estimation"
    - "model slicing for supporting complex analytics with elastic inference cost and resource constraints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-5 (induced by source paper "dynamic inference a new approach toward efficient video action recognition"):
  with members:
    - "multi agent reinforcement learning based frame sampling for effective untrimmed video recognition"
    - "eco efficient convolutional network for online video understanding"
    - "multi fiber networks for video recognition"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "motion feature network fixed motion filter for action recognition"
    - "tsm temporal shift module for efficient video understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"

Connected through hyperedge HE-6 (induced by source paper "fractional skipping towards finer grained dynamic cnn inference"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "slimmable neural networks"
    - "hybrid pruning thinner sparse networks for fast inference on edge devices"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "haq hardware aware automated quantization with mixed precision"
    - "gaternet dynamic filter selection in convolutional neural network via a dedicated global gating network"
    - "skipnet learning dynamic routing in convolutional networks"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-7 (induced by source paper "localizing multi scale semantic patches for image classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "saccader improving accuracy of hard attention models for vision"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "searching for mobilenetv3"
    - "gated convolutional networks with hybrid connectivity for image classification"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-8 (induced by source paper "autoslim towards one shot architecture search for channel numbers"):
  with members:
    - "universally slimmable networks and improved training techniques"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "learning to prune filters in convolutional neural networks"
    - "slimmable neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "graph hypernetworks for neural architecture search"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

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)