human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "sgdr stochastic gradient descent with restarts":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "deep networks with stochastic depth":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on feature normalization and data augmentation")
  Member "identity mappings in deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lossless compression of deep neural networks")
  Member "an empirical evaluation of deep learning on highway driving":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semi local 3d lane detection and uncertainty estimation")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "equilibrated adaptive learning rates for non convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deam adaptive momentum with discriminative weight for stochastic optimization")

Question: Please tell me which class the source paper 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)