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: "towards robust neural networks via random self ensemble"
  Abstract: Recent studies have revealed the vulnerability of deep neural networks - A small adversarial perturbation that is imperceptible to human can easily make a well-trained deep neural network mis-classify. This makes it unsafe to apply neural networks in security-critical applications. In this paper, we propose a new defensive algorithm called Random Self-Ensemble (RSE) by combining two important concepts: ${\bf randomness}$ and ${\bf ensemble}$. To protect a targeted model, RSE adds random noise layers to the neural network to prevent from state-of-the-art gradient-based attacks, and ensembles the prediction over random noises to stabilize the performance. We show that our algorithm is equivalent to ensemble an infinite number of noisy models $f_\epsilon$ without any additional memory overhead, and the proposed training procedure based on noisy stochastic gradient descent can ensure the ensemble model has good predictive capability. Our algorithm significantly outperforms previous defense techniques on real datasets. For instance, on CIFAR-10 with VGG network (which has $92\%$ accuracy without any attack), under the state-of-the-art C&W attack within a certain distortion tolerance, the accuracy of unprotected model drops to less than $10\%$, the best previous defense technique has $48\%$ accuracy, while our method still has $86\%$ prediction accuracy under the same level of attack. Finally, our method is simple and easy to integrate into any neural network.

Connected through hyperedge HE-1 (induced by source paper "black box smoothing a provable defense for pretrained classifiers"):
  with members:
    - "macer attack free and scalable robust training via maximizing certified radius"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "simple black box adversarial attacks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "adversarial risk and the dangers of evaluating against weak attacks"

Connected through hyperedge HE-2 (induced by source paper "ghostimage perception domain attacks against vision based object classification systems"):
  with members:
    - "taxonomy and challenges of out of band signal injection attacks and defenses"
    - "adversarial sensor attack on lidar based perception in autonomous driving"
    - "making an invisibility cloak real world adversarial attacks on object detectors"
    - "are self driving cars secure evasion attacks against deep neural networks for steering angle prediction"
    - "advhat real world adversarial attack on arcface face id system"
    - "spatially transformed adversarial examples"
    - "stochastic activation pruning for robust adversarial defense"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "randomized smoothing of all shapes and sizes"):
  with members:
    - "curse of dimensionality on randomized smoothing for certifiable robustness"
    - "macer attack free and scalable robust training via maximizing certified radius"
    - "robustness certificates for sparse adversarial attacks by randomized ablation"
    - "certified adversarial robustness via randomized smoothing"
    - "wasserstein smoothing certified robustness against wasserstein adversarial attacks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"

Connected through hyperedge HE-4 (induced by source paper "an empirical evaluation of perturbation based defenses"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "defending against whitebox adversarial attacks via randomized discretization"
    - "ppd permutation phase defense against adversarial examples in deep learning"
    - "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"
    - "feature distillation dnn oriented jpeg compression against adversarial examples"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "the effects of jpeg and jpeg2000 compression on attacks using adversarial examples"

Connected through hyperedge HE-5 (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-6 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing"):
  with members:
    - "adversarial examples on graph data deep insights into attack and defense"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"
    - "adversarial attacks on graph neural networks via meta learning"
    - "certifiable robustness to graph perturbations"
    - "attacking graph based classification via manipulating the graph structure"

Connected through hyperedge HE-7 (induced by source paper "cat customized adversarial training for improved robustness"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "sign opt a query efficient hard label adversarial attack"
    - "label smoothing and logit squeezing a replacement for adversarial training"
    - "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "certified adversarial robustness via randomized smoothing"
    - "convergence of adversarial training in overparametrized neural networks"
    - "theoretically principled trade off between robustness and accuracy"

Connected through hyperedge HE-8 (induced by source paper "macer attack free and scalable robust training via maximizing certified radius"):
  with members:
    - "mma training direct input space margin maximization through adversarial training"
    - "certified adversarial robustness via randomized smoothing"
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "towards stable and efficient training of verifiably robust neural networks"
    - "scaling provable adversarial defenses"

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)