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: "do deep nets really need weight decay and dropout"
  Abstract: The impressive success of modern deep neural networks on computer vision tasks has been achieved through models of very large capacity compared to the number of available training examples. This overparameterization is often said to be controlled with the help of different regularization techniques, mainly weight decay and dropout. However, since these techniques reduce the effective capacity of the model, typically even deeper and wider architectures are required to compensate for the reduced capacity. Therefore, there seems to be a waste of capacity in this practice. In this paper we build upon recent research that suggests that explicit regularization may not be as important as widely believed and carry out an ablation study that concludes that weight decay and dropout may not be necessary for object recognition if enough data augmentation is introduced.

Connected through hyperedge HE-1 (induced by source paper "truth or backpropaganda an empirical investigation of deep learning theory"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "fixup initialization residual learning without normalization"
    - "understanding generalization through visualizations"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "gradient descent finds global minima of deep neural networks"
    - "the singular values of convolutional layers"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-2 (induced by source paper "adversarial learning of general transformations for data augmentation"):
  with members:
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "dada deep adversarial data augmentation for extremely low data regime classification"
    - "data augmentation instead of explicit regularization"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "good semi supervised learning that requires a bad gan"
    - "triple generative adversarial nets"
    - "the effectiveness of data augmentation in image classification using deep learning"

Connected through hyperedge HE-3 (induced by source paper "data augmentation instead of explicit regularization"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "further advantages of data augmentation on convolutional neural networks"
    - "mixup as locally linear out of manifold regularization"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "spectrally normalized margin bounds for neural networks"
    - "improved regularization of convolutional neural networks with cutout"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-4 (induced by source paper "further advantages of data augmentation on convolutional neural networks"):
  with members:
    - "few shot learning with metric agnostic conditional embeddings"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "improved regularization of convolutional neural networks with cutout"
    - "understanding deep learning requires rethinking generalization"
    - "wide residual networks"
    - "deep image scaling up image recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "toward deeper understanding of nonconvex stochastic optimization with momentum using diffusion approximations"
    - "three factors influencing minima in sgd"
    - "stochastic hyperparameter optimization through hypernetworks"
    - "don t decay the learning rate increase the batch size"
    - "super convergence very fast training of residual networks using large learning rates"
    - "residual connections encourage iterative inference"
    - "a bayesian perspective on generalization and stochastic gradient descent"

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)