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: "ensemble robustness of deep learning algorithms"
  Abstract: The question why deep learning algorithms perform so well in practice has attracted increasing research interest. However, most of well-established approaches, such as hypothesis capacity, robustness or sparseness, have not provided complete explanations, due to the high complexity of the deep learning algorithms and their inherent randomness. In this work, we introduce a new approach~\textendash~ensemble robustness~\textendash~towards characterizing the generalization performance of generic deep learning algorithms. Ensemble robustness concerns robustness of the \emph{population} of the hypotheses that may be output by a learning algorithm. Through the lens of ensemble robustness, we reveal that a stochastic learning algorithm can generalize well as long as its sensitiveness to adversarial perturbation is bounded in average, or equivalently, the performance variance of the algorithm is small. Quantifying ensemble robustness of various deep learning algorithms may be difficult analytically. However, extensive simulations for seven common deep learning algorithms for different network architectures provide supporting evidence for our claims. Furthermore, our work explains the good performance of several published deep learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "on model stability as a function of random seed"):
  with members:
    - "an analysis of attention over clinical notes for predictive tasks"
    - "attention is not explanation"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "averaging weights leads to wider optima and better generalization"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "attention is all you need"
    - "fasttext zip compressing text classification models"
    - "ensemble robustness and generalization of stochastic deep learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "eagleeye attack agnostic defense against adversarial inputs technical report"):
  with members:
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "adversarial manipulation of deep representations"
    - "draw a recurrent neural network for image generation"
    - "exploring the space of adversarial images"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-3 (induced by source paper "attack and defense of dynamic analysis based adversarial neural malware classification models"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "hardening classifiers against evasion the good the bad and the ugly"
    - "on the statistical detection of adversarial examples"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "stealing machine learning models via prediction apis"
    - "practical black box attacks against deep learning systems using adversarial examples"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "distillation as a defense to adversarial perturbations against deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "a pac bayesian analysis of randomized learning with application to stochastic gradient descent"):
  with members:
    - "data dependent stability of stochastic gradient descent"
    - "optimal learning for multi pass stochastic gradient methods"
    - "generalization properties and implicit regularization for multiple passes sgm"
    - "minimizing the maximal loss how and why"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-5 (induced by source paper "deep online convex optimization with gated games"):
  with members:
    - "efficient second order online learning by sketching"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "benefits of depth in neural networks"
    - "strongly typed recurrent neural networks"
    - "conditional computation in neural networks for faster models"
    - "the power of depth for feedforward neural networks"
    - "deep learning in neural networks"
    - "fast convergence of regularized learning in games"

Connected through hyperedge HE-6 (induced by source paper "measuring neural net robustness with constraints"):
  with members:
    - "practical black box attacks against machine learning"
    - "adversarial manipulation of deep representations"
    - "exploring the space of adversarial images"
    - "caffe convolutional architecture for fast feature embedding"
    - "towards deep neural network architectures robust to adversarial examples"
    - "intriguing properties of neural networks"
    - "robustness and generalization"

Connected through hyperedge HE-7 (induced by source paper "semi supervised deep learning by metric embedding"):
  with members:
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "rethinking the inception architecture for computer vision"

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)