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: "super convergence very fast training of residual networks using large learning rates"
  Abstract: In this paper, we show a phenomenon, which we named "super-convergence", where residual networks can be trained using an order of magnitude fewer iterations than is used with standard training methods. The existence of super-convergence is relevant to understanding why deep networks generalize well. One of the key elements of super-convergence is training with cyclical learning rates and a large maximum learning rate. Furthermore, we present evidence that training with large learning rates improves performance by regularizing the network. In addition, we show that super-convergence provides a greater boost in performance relative to standard training when the amount of labeled training data is limited. We also derive a simplification of the Hessian Free optimization method to compute an estimate of the optimal learning rate. The architectures and code to replicate the figures in this paper are available at github.com/lnsmith54/super-convergence.

Connected through hyperedge HE-1 (induced by source paper "rethinking the hyperparameters for fine tuning"):
  with members:
    - "task2vec task embedding for meta learning"
    - "rethinking imagenet pre training"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "do better imagenet models transfer better"
    - "bag of tricks for image classification with convolutional neural networks"
    - "toward deeper understanding of nonconvex stochastic optimization with momentum using diffusion approximations"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "domain adaptive transfer learning with specialist models"

Connected through hyperedge HE-2 (induced by source paper "fast is better than free revisiting adversarial training"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "certified adversarial robustness via randomized smoothing"
    - "me net towards effective adversarial robustness with matrix estimation"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "adversarial training for free"
    - "adversarial robustness against the union of multiple perturbation models"
    - "on evaluating adversarial robustness"
    - "scaling provable adversarial defenses"

Connected through hyperedge HE-3 (induced by source paper "pointtracknet an end to end network for 3 d object detection and tracking from point clouds"):
  with members:
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "pointit a fast tracking framework based on 3d instance segmentation"
    - "autotrack a lightweight object detection and tracking system for the sae autodrive challenge"
    - "fantrack 3d multi object tracking with feature association network"
    - "end to end learning of multi sensor 3d tracking by detection"
    - "flownet3d learning scene flow in 3d point clouds"
    - "birdnet a 3d object detection framework from lidar information"
    - "focal loss in 3d object detection"

Connected through hyperedge HE-4 (induced by source paper "the two regimes of deep network training"):
  with members:
    - "an exponential learning rate schedule for deep learning"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "measuring the effects of data parallelism on neural network training"
    - "a bayesian perspective on generalization and stochastic gradient descent"
    - "sgdr stochastic gradient descent with restarts"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "stochastic runge kutta methods and adaptive sgd g2 stochastic gradient descent"):
  with members:
    - "assessing four neural networks on handwritten digit recognition dataset mnist"
    - "deep neural networks motivated by partial differential equations"
    - "convolutional neural networks combined with runge kutta methods"
    - "stochastic modified equations and dynamics of stochastic gradient algorithms i mathematical foundations"
    - "cyclical learning rates for training neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "gate decorator global filter pruning method for accelerating deep convolutional neural networks"):
  with members:
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-7 (induced by source paper "robust multi modality multi object tracking"):
  with members:
    - "deep multi sensor lane detection"
    - "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "end to end learning of multi sensor 3d tracking by detection"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "combined image and world space tracking in traffic scenes"
    - "the perfect match 3d point cloud matching with smoothed densities"

Connected through hyperedge HE-8 (induced by source paper "stage based hyper parameter optimization for deep learning"):
  with members:
    - "analysis of large scale multi tenant gpu clusters for dnn training workloads"
    - "massively parallel hyperparameter tuning"
    - "mltuner system support for automatic machine learning tuning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tune a research platform for distributed model selection and training"
    - "chopt automated hyperparameter optimization framework for cloud based machine learning platforms"
    - "online learning rate adaptation with hypergradient descent"
    - "progressive growing of gans for improved quality stability and variation"

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)