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: "on the depth of deep neural networks a theoretical view"
  Abstract: People believe that depth plays an important role in success of deep neural networks (DNN). However, this belief lacks solid theoretical justifications as far as we know. We investigate role of depth from perspective of margin bound. In margin bound, expected error is upper bounded by empirical margin error plus Rademacher Average (RA) based capacity term. First, we derive an upper bound for RA of DNN, and show that it increases with increasing depth. This indicates negative impact of depth on test performance. Second, we show that deeper networks tend to have larger representation power (measured by Betti numbers based complexity) than shallower networks in multi-class setting, and thus can lead to smaller empirical margin error. This implies positive impact of depth. The combination of these two results shows that for DNN with restricted number of hidden units, increasing depth is not always good since there is a tradeoff between positive and negative impacts. These results inspire us to seek alternative ways to achieve positive impact of depth, e.g., imposing margin-based penalty terms to cross entropy loss so as to reduce empirical margin error without increasing depth. Our experiments show that in this way, we achieve significantly better test performance.

Connected through hyperedge HE-1 (induced by source paper "learn2perturb an end to end feature perturbation learning to improve adversarial robustness"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "robust neural networks using randomized adversarial training"
    - "theoretical evidence for adversarial robustness through randomization the case of the exponential family"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "feature denoising for improving adversarial robustness"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "black box adversarial attacks with limited queries and information"
    - "audio adversarial examples targeted attacks on speech to text"

Connected through hyperedge HE-2 (induced by source paper "mri super resolution with gan and 3d multi level densenet smaller faster and better"):
  with members:
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "brain mri super resolution using 3d deep densely connected neural networks"
    - "learning a mixture of deep networks for single image super resolution"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "enhanced deep residual networks for single image super resolution"
    - "improved training of wasserstein gans"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-3 (induced by source paper "neural network representation control gaussian isolation machines and cvc regularization"):
  with members:
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "activation functions comparison of trends in practice and research for deep learning"
    - "anomaly detection using one class neural networks"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "robust deep and inductive anomaly detection"
    - "generic black box end to end attack against state of the art api call based malware classifiers"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

Connected through hyperedge HE-4 (induced by source paper "network implosion effective model compression for resnets via static layer pruning and retraining"):
  with members:
    - "learning strict identity mappings in deep residual networks"
    - "data driven sparse structure selection for deep neural networks"
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "convolutional sequence to sequence learning"
    - "adaptive learning rate via covariance matrix based preconditioning for deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-5 (induced by source paper "controlling neural level sets"):
  with members:
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "theoretically principled trade off between robustness and accuracy"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "learning implicit fields for generative shape modeling"

Connected through hyperedge HE-6 (induced by source paper "understanding generalization through visualizations"):
  with members:
    - "identifying generalization properties in neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "averaging weights leads to wider optima and better generalization"
    - "certified defenses for data poisoning attacks"
    - "deep linear neural networks with arbitrary loss all local minima are global"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "data dependent stability of stochastic gradient descent"

Connected through hyperedge HE-7 (induced by source paper "adversarial margin maximization networks"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "max margin adversarial mma training direct input space margin maximization through adversarial training"
    - "sok security and privacy in machine learning"
    - "sphereface deep hypersphere embedding for face recognition"
    - "aggregated residual transformations for deep neural networks"
    - "polynet a pursuit of structural diversity in very deep networks"
    - "towards evaluating the robustness of neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "exploring the ideal depth of neural network when predicting question deletion on community question answering"):
  with members:
    - "benefits of depth in neural networks"
    - "wide residual networks"
    - "training very deep networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "fitnets hints for thin deep nets"
    - "very deep convolutional networks for large scale image recognition"
    - "scalable high quality object detection"
    - "chaff from the wheat characterization and modeling of deleted questions on stack overflow"

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)