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: "fault sneaking attack a stealthy framework for misleading deep neural networks"
  Abstract: Despite the great achievements of deep neural networks (DNNs), the vulnerability of state-of-the-art DNNs raises security concerns of DNNs in many application domains requiring high reliability. We propose the fault sneaking attack on DNNs, where the adversary aims to misclassify certain input images into any target labels by modifying the DNN parameters. We apply ADMM (alternating direction method of multipliers) for solving the optimization problem of the fault sneaking attack with two constraints: 1) the classification of the other images should be unchanged and 2) the parameter modifications should be minimized. Specifically, the first constraint requires us not only to inject designated faults (misclassifications), but also to hide the faults for stealthy or sneaking considerations by maintaining model accuracy. The second constraint requires us to minimize the parameter modifications (using 0 norm to measure the number of modifications and 2 norm to measure the magnitude of modifications). Comprehensive experimental evaluation demonstrates that the proposed framework can inject multiple sneaking faults without losing the overall test accuracy performance.CCS CONCEPTS•Security and privacy → Domain-specific security and privacy architectures;Network security; •Networks → Network performance analysis; •Theory of computation → Theory and algorithms for application domains;

Connected through hyperedge HE-1 (induced by source paper "towards query efficient black box adversary with zeroth order natural gradient descent"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "hessian aware zeroth order optimization for black box adversarial attack"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "defensive dropout for hardening deep neural networks under adversarial attacks"
    - "black box adversarial attacks with limited queries and information"
    - "structured adversarial attack towards general implementation and better interpretability"

Connected through hyperedge HE-2 (induced by source paper "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "defensive quantization when efficiency meets robustness"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "defensive dropout for hardening deep neural networks under adversarial attacks"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "exploring the space of black box attacks on deep neural networks"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-3 (induced by source paper "protecting neural networks with hierarchical random switching towards better robustness accuracy trade off for stochastic defenses"):
  with members:
    - "defensive dropout for hardening deep neural networks under adversarial attacks"
    - "adversarial reprogramming of neural networks"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "an admm based universal framework for adversarial attacks on deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "synthesizing robust adversarial examples"
    - "towards robust neural networks via random self ensemble"

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)