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: "regularizing deep neural networks by noise its interpretation and optimization"
  Abstract: Overfitting is one of the most critical challenges in deep neural networks, and there are various types of regularization methods to improve generalization performance. Injecting noises to hidden units during training, e.g., dropout, is known as a successful regularizer, but it is still not clear enough why such training techniques work well in practice and how we can maximize their benefit in the presence of two conflicting objectives---optimizing to true data distribution and preventing overfitting by regularization. This paper addresses the above issues by 1) interpreting that the conventional training methods with regularization by noise injection optimize the lower bound of the true objective and 2) proposing a technique to achieve a tighter lower bound using multiple noise samples per training example in a stochastic gradient descent iteration. We demonstrate the effectiveness of our idea in several computer vision applications.

Connected through hyperedge HE-1 (induced by source paper "perceptual image super resolution with progressive adversarial network"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "analyzing and improving the image quality of stylegan"
    - "deep learning for image super resolution a survey"
    - "zoom to learn learn to zoom"
    - "camera lens super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "fast accurate and lightweight super resolution with cascading residual network"

Connected through hyperedge HE-2 (induced by source paper "noisy machines understanding noisy neural networks and enhancing robustness to analog hardware errors using distillation"):
  with members:
    - "zero shot knowledge transfer via adversarial belief matching"
    - "adversarially robust distillation"
    - "accurate deep neural network inference using computational phase change memory"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "neuromorphic computing with multi memristive synapses"
    - "mixed precision in memory computing"
    - "all optical machine learning using diffractive deep neural networks"
    - "model compression via distillation and quantization"

Connected through hyperedge HE-3 (induced by source paper "adaptive regularization via residual smoothing in deep learning optimization"):
  with members:
    - "combining learning rate decay and weight decay with complexity gradient descent part i"
    - "towards understanding regularization in batch normalization"
    - "understanding batch normalization"
    - "audio visual scene analysis with self supervised multisensory features"
    - "unsupervised learning of depth and ego motion from video"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "synthetic vs real deep learning on controlled noise"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "unsupervised label noise modeling and loss correction"
    - "using pre training can improve model robustness and uncertainty"
    - "robust neural machine translation with doubly adversarial inputs"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep learning is robust to massive label noise"

Connected through hyperedge HE-5 (induced by source paper "adaptivfloat a floating point based data type for resilient deep learning inference"):
  with members:
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "deep positron a deep neural network using the posit number system"
    - "effective quantization approaches for recurrent neural networks"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "wrpn wide reduced precision networks"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"

Connected through hyperedge HE-6 (induced by source paper "adaptive weight decay for deep neural networks"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "don t decay the learning rate increase the batch size"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "sharp minima can generalize for deep nets"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-7 (induced by source paper "regularizing neural networks via stochastic branch layers"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep networks with stochastic depth"
    - "improved dropout for shallow and deep learning"
    - "dropout with expectation linear regularization"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "sequential neural networks as automata"):
  with members:
    - "investigating capsule networks with dynamic routing for text classification"
    - "rational recurrences"
    - "context free transductions with neural stacks"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "attention is all you need"
    - "effective approaches to attention based neural machine translation"
    - "character aware neural language models"
    - "attention based models for speech recognition"

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)