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: "no more pesky learning rate guessing games"
  Abstract: It is known that the learning rate is the most important hyper-parameter to tune for training deep convolutional neural networks (i.e., a "guessing game"). This report describes a new method for setting the learning rate, named cyclical learning rates, that eliminates the need to experimentally find the best values and schedule for the learning rates. Instead of setting the learning rate to fixed values, this method lets the learning rate cyclically vary within reasonable boundary values. This report shows that training with cyclical learning rates achieves near optimal classification accuracy without tuning and often in many fewer iterations. This report also describes a simple way to estimate "reasonable bounds" - by linearly increasing the learning rate in one training run of the network for only a few epochs. In addition, cyclical learning rates are demonstrated on training with the CIFAR-10 dataset and the AlexNet and GoogLeNet architectures on the ImageNet dataset. These methods are practical tools for everyone who trains convolutional neural networks.

Connected through hyperedge HE-1 (induced by source paper "fastai a layered api for deep learning"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "developing an app to interpret chest x rays to support the diagnosis of respiratory pathology with artificial intelligence"
    - "suggestion mining from online reviews using ulmfit"
    - "multiclass language identification using deep learning on spectral images of audio signals"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "mish a self regularized non monotonic neural activation function"
    - "bag of tricks for image classification with convolutional neural networks"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-2 (induced by source paper "batchensemble an alternative approach to efficient ensemble and lifelong learning"):
  with members:
    - "analyzing the role of model uncertainty for electronic health records"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "evaluating scalable bayesian deep learning methods for robust computer vision"
    - "experience replay for continual learning"
    - "efficient lifelong learning with a gem"
    - "reinforced continual learning"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-3 (induced by source paper "image based size analysis of agglomerated and partially sintered particles via convolutional neural networks"):
  with members:
    - "apple picker automatic particle picking a low effort cryo em framework"
    - "fully automated primary particle size analysis of agglomerates on transmission electron microscopy images via artificial neural networks"
    - "an analysis of deep neural network models for practical applications"
    - "mask r cnn"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-4 (induced by source paper "a continuous space neural language model for bengali language"):
  with members:
    - "fine tuned language models for text classification"
    - "regularizing and optimizing lstm language models"
    - "automated word prediction in bangla language using stochastic language models"
    - "character level convolutional networks for text classification"
    - "recurrent neural network regularization"

Connected through hyperedge HE-5 (induced by source paper "automated detection of pitting and stress corrosion cracks in used nuclear fuel dry storage canisters using residual neural networks"):
  with members:
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "sa text simple but accurate detector for text of arbitrary shapes"):
  with members:
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "textfield learning a deep direction field for irregular scene text detection"
    - "convolutional character networks"
    - "character region awareness for text detection"
    - "efficient and accurate arbitrary shaped text detection with pixel aggregation network"
    - "towards robust curve text detection with conditional spatial expansion"
    - "shape robust text detection with progressive scale expansion network"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"

Connected through hyperedge HE-7 (induced by source paper "on generalizing detection models for unconstrained environments"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "centernet keypoint triplets for object detection"
    - "scale aware trident networks for object detection"
    - "incremental learning in person re identification"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "fine tuned language models for text classification"
    - "sniper efficient multi scale training"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-8 (induced by source paper "forget the learning rate decay loss"):
  with members:
    - "fast better training trick random gradient"
    - "super convergence very fast training of residual networks using large learning rates"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "energy based generative adversarial network"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"
    - "pyramid scene parsing network"
    - "effective approaches to attention based neural machine translation"

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)