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: "curriculum adversarial training"
  Abstract: Recently, deep learning has been applied to many security-sensitive applications, such as facial authentication. The existence of adversarial examples hinders such applications. The state-of-the-art result on defense shows that adversarial training can be applied to train a robust model on MNIST against adversarial examples; but it fails to achieve a high empirical worst-case accuracy on a more complex task, such as CIFAR-10 and SVHN. In our work, we propose curriculum adversarial training (CAT) to resolve this issue. The basic idea is to develop a curriculum of adversarial examples generated by attacks with a wide range of strengths. With two techniques to mitigate the forgetting and the generalization issues, we demonstrate that CAT can improve the prior art's empirical worst-case accuracy by a large margin of 25% on CIFAR-10 and 35% on SVHN. At the same, the model's performance on non-adversarial inputs is comparable to the state-of-the-art models.

Connected through hyperedge HE-1 (induced by source paper "mga momentum gradient attack on network"):
  with members:
    - "unsupervised euclidean distance attack on network embedding"
    - "things you may not know about adversarial example a black box adversarial image attack"
    - "adversarial attack on graph structured data"
    - "adversarial training towards robust multimedia recommender system"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "poisoning attacks to graph based recommender systems"
    - "ga based q attack on community detection"
    - "adversarial examples that fool both computer vision and time limited humans"

Connected through hyperedge HE-2 (induced by source paper "improving the affordability of robustness training for dnns"):
  with members:
    - "improving adversarial robustness via promoting ensemble diversity"
    - "exploring the hyperparameter landscape of adversarial robustness"
    - "adversarial training for free"
    - "using pre training can improve model robustness and uncertainty"
    - "adversarially robust transfer learning"
    - "scaling provable adversarial defenses"
    - "towards the first adversarially robust neural network model on mnist"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"

Connected through hyperedge HE-3 (induced by source paper "defending against physically realizable attacks on image classification"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "simple physical adversarial examples against end to end autonomous driving models"
    - "scaling provable adversarial defenses"
    - "sentinet detecting physical attacks against deep learning systems"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "efficient formal safety analysis of neural networks"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "evolutionarily curated curriculum learning for deep reinforcement learning agents"):
  with members:
    - "talakat bullet hell generation through constrained map elites"
    - "thinking fast and slow with deep learning and tree search"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-5 (induced by source paper "distributionally adversarial attack"):
  with members:
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "tackling over pruning in variational autoencoders"
    - "stein variational policy gradient"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "monge beats bayes hardness results for adversarial training"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "unravelling robustness of deep learning based face recognition against adversarial attacks"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "spectral normalization for generative adversarial networks"
    - "mixup beyond empirical risk minimization"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"

Connected through hyperedge HE-7 (induced by source paper "an optimal control view of adversarial machine learning"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

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)