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: "convergence of adversarial training in overparametrized neural networks"
  Abstract: Neural networks are vulnerable to adversarial examples, i.e. inputs that are imperceptibly perturbed from natural data and yet incorrectly classified by the network. Adversarial training, a heuristic form of robust optimization that alternates between minimization and maximization steps, has proven to be among the most successful methods to train networks to be robust against a pre-defined family of perturbations. This paper provides a partial answer to the success of adversarial training, by showing that it converges to a network where the surrogate loss with respect to the the attack algorithm is within $\epsilon$ of the optimal robust loss. Then we show that the optimal robust loss is also close to zero, hence adversarial training finds a robust classifier. The analysis technique leverages recent work on the analysis of neural networks via Neural Tangent Kernel (NTK), combined with motivation from online-learning when the maximization is solved by a heuristic, and the expressiveness of the NTK kernel in the $\ell_\infty$-norm. In addition, we also prove that robust interpolation requires more model capacity, supporting the evidence that adversarial training requires wider networks.

Connected through hyperedge HE-1 (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"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial robustness via adversarial label smoothing"

Connected through hyperedge HE-2 (induced by source paper "intriguing properties of adversarial training"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "are labels required for improving adversarial robustness"
    - "defending adversarial attacks by correcting logits"
    - "adversarially robust generalization just requires more unlabeled data"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial examples improve image recognition"

Connected through hyperedge HE-3 (induced by source paper "adversarially robust generalization just requires more unlabeled data"):
  with members:
    - "adversarial training for free"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "adversarial vulnerability for any classifier"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "non local neural networks"
    - "focal loss for dense object detection"

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)