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: "scaling up the randomized gradient free adversarial attack reveals overestimation of robustness using established attacks"
  Abstract: Modern neural networks are highly non-robust against adversarial manipulation. A significant amount of work has been invested in techniques to compute lower bounds on robustness through formal guarantees and to build provably robust model. However it is still difficult to apply them to larger networks or in order to get robustness against larger perturbations. Thus attack strategies are needed to provide tight upper bounds on the actual robustness. We significantly improve the randomized gradient-free attack for ReLU networks [9], in particular by scaling it up to large networks. We show that our attack achieves similar or significantly smaller robust accuracy than state-of-the-art attacks like PGD or the one of Carlini and Wagner, thus revealing an overestimation of the robustness by these state-of-the-art methods. Our attack is not based on a gradient descent scheme and in this sense gradient-free, which makes it less sensitive to the choice of hyperparameters as no careful selection of the stepsize is required.

Connected through hyperedge HE-1 (induced by source paper "reliable evaluation of adversarial robustness with an ensemble of diverse parameter free attacks"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "controlling neural level sets"
    - "metric learning for adversarial robustness"
    - "certified adversarial robustness via randomized smoothing"
    - "your classifier is secretly an energy based model and you should treat it like one"
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "jacobian adversarially regularized networks for robustness"

Connected through hyperedge HE-2 (induced by source paper "provable robustness against all adversarial l_p perturbations for p geq 1"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "transfer of adversarial robustness between perturbation types"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "minimally distorted adversarial examples with a fast adaptive boundary attack"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "adversarial patch"
    - "logit pairing methods can fool gradient based attacks"

Connected through hyperedge HE-3 (induced by source paper "minimally distorted adversarial examples with a fast adaptive boundary attack"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "one pixel attack for fooling deep neural networks"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial patch"
    - "logit pairing methods can fool gradient based attacks"
    - "sparsefool a few pixels make a big difference"
    - "distributionally adversarial attack"

Connected through hyperedge HE-4 (induced by source paper "provable robustness of relu networks via maximization of linear regions"):
  with members:
    - "scaling provable adversarial defenses"
    - "logit pairing methods can fool gradient based attacks"
    - "a randomized gradient free attack on relu networks"
    - "training for faster adversarial robustness verification via inducing relu stability"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "adversarial examples are not easily detected bypassing ten detection methods"

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)