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: "adding noise to the input of a model trained with a regularized objective"
  Abstract: Regularization is a well studied problem in the context of neural networks. It is usually used to improve the generalization performance when the number of input samples is relatively small or heavily contaminated with noise. The regularization of a parametric model can be achieved in different manners some of which are early stopping (Morgan and Bourlard, 1990), weight decay, output smoothing that are used to avoid overfitting during the training of the considered model. From a Bayesian point of view, many regularization techniques correspond to imposing certain prior distributions on model parameters (Krogh and Hertz, 1991). Using Bishop's approximation (Bishop, 1995) of the objective function when a restricted type of noise is added to the input of a parametric function, we derive the higher order terms of the Taylor expansion and analyze the coefficients of the regularization terms induced by the noisy input. In particular we study the effect of penalizing the Hessian of the mapping function with respect to the input in terms of generalization performance. We also show how we can control independently this coefficient by explicitly penalizing the Jacobian of the mapping function on corrupted inputs.

Connected through hyperedge HE-1 (induced by source paper "shakeout a new approach to regularized deep neural network training"):
  with members:
    - "a deep and autoregressive approach for topic modeling of multimodal data"
    - "hcp a flexible cnn framework for multi label image classification"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "sparse activity and sparse connectivity in supervised learning"
    - "improved dropout for shallow and deep learning"
    - "wide residual networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-2 (induced by source paper "information based deep clustering an experimental study"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "boosting self supervised learning via knowledge transfer"
    - "sentinet detecting physical attacks against deep learning systems"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "deep clustering for unsupervised learning of visual features"
    - "iterative reorganization with weak spatial constraints solving arbitrary jigsaw puzzles for unsupervised representation learning"
    - "mine mutual information neural estimation"

Connected through hyperedge HE-3 (induced by source paper "implicit regularization for deep neural networks driven by an ornstein uhlenbeck like process"):
  with members:
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "data dependent stability of stochastic gradient descent"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "algorithmic regularization in over parameterized matrix sensing and neural networks with quadratic activations"
    - "understanding deep learning requires rethinking generalization"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-4 (induced by source paper "towards understanding regularization in batch normalization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "switchable whitening for deep representation learning"
    - "do normalization layers in a deep convnet really need to be distinct"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "differentiable learning to normalize via switchable normalization"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "electron proton dynamics in deep learning"
    - "l2 regularization versus batch and weight normalization"

Connected through hyperedge HE-5 (induced by source paper "regularization for deep learning a taxonomy"):
  with members:
    - "proximal backpropagation"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "deep unsupervised learning through spatial contrasting"
    - "adjusting for dropout variance in batch normalization and weight initialization"
    - "synthesizing robust adversarial examples"
    - "an overview of multi task learning in deep neural networks"
    - "densely connected convolutional networks"
    - "trivializing the energy landscape of deep networks"

Connected through hyperedge HE-6 (induced by source paper "feature grouping as a stochastic regularizer for high dimensional structured data"):
  with members:
    - "a convolutional neural network for modelling sentences"
    - "playing atari with deep reinforcement learning"
    - "small sample brain mapping sparse recovery on spatially correlated designs with randomization and clustering"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "structured sparsity through convex optimization"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-7 (induced by source paper "using feature grouping as a stochastic regularizer for high dimensional noisy data"):
  with members:
    - "a convolutional neural network for modelling sentences"
    - "playing atari with deep reinforcement learning"
    - "small sample brain mapping sparse recovery on spatially correlated designs with randomization and clustering"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "structured sparsity through convex optimization"

Connected through hyperedge HE-8 (induced by source paper "block cyclic stochastic coordinate descent for deep neural networks"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "aggregated residual transformations for deep neural networks"
    - "identity mappings in deep residual networks"
    - "big batch sgd automated inference using adaptive batch sizes"

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.AI(Artificial Intelligence)