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: "stochastic substitute training a gray box approach to craft adversarial examples against gradient obfuscation defenses"
  Abstract: It has been shown that adversaries can craft example inputs to neural networks which are similar to legitimate inputs but have been created to purposely cause the neural network to misclassify the input. These adversarial examples are crafted, for example, by calculating gradients of a carefully defined loss function with respect to the input. As a countermeasure, some researchers have tried to design robust models by blocking or obfuscating gradients, even in white-box settings. Another line of research proposes introducing a separate detector to attempt to detect adversarial examples. This approach also makes use of gradient obfuscation techniques, for example, to prevent the adversary from trying to fool the detector. In this paper, we introduce stochastic substitute training, a gray-box approach that can craft adversarial examples for defenses which obfuscate gradients. For those defenses that have tried to make models more robust, with our technique, an adversary can craft adversarial examples with no knowledge of the defense. For defenses that attempt to detect the adversarial examples, with our technique, an adversary only needs very limited information about the defense to craft adversarial examples. We demonstrate our technique by applying it against two defenses which make models more robust and two defenses which detect adversarial examples.

Connected through hyperedge HE-1 (induced by source paper "making targeted black box evasion attacks effective and efficient"):
  with members:
    - "copy and paste a simple but effective initialization method for black box adversarial attacks"
    - "reducing adversarial example transferability using gradient regularization"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "model reuse attacks on deep learning systems"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "query efficient black box attack by active learning"
    - "prada protecting against dnn model stealing attacks"
    - "genattack practical black box attacks with gradient free optimization"

Connected through hyperedge HE-2 (induced by source paper "defense methods against adversarial examples for recurrent neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "textbugger generating adversarial text against real world applications"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generating natural language adversarial examples"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "robust neural malware detection models for emulation sequence learning"

Connected through hyperedge HE-3 (induced by source paper "understanding adversarial robustness the trade off between minimum and average margin"):
  with members:
    - "towards robust locally linear deep networks"
    - "scaling provable adversarial defenses"
    - "sparse dnns with improved adversarial robustness"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "provable robustness of relu networks via maximization of linear regions"

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)