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: "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"
  Abstract: Robust machine learning is currently one of the most prominent topics which could potentially help shaping a future of advanced AI platforms that not only perform well in average cases but also in worst cases or adverse situations. Despite the long-term vision, however, existing studies on black-box adversarial attacks are still restricted to very specific settings of threat models (e.g., single distortion metric and restrictive assumption on target model's feedback to queries) and/or suffer from prohibitively high query complexity. To push for further advances in this field, we introduce a general framework based on an operator splitting method, the alternating direction method of multipliers (ADMM) to devise efficient, robust black-box attacks that work with various distortion metrics and feedback settings without incurring high query complexity. Due to the black-box nature of the threat model, the proposed ADMM solution framework is integrated with zeroth-order (ZO) optimization and Bayesian optimization (BO), and thus is applicable to the gradient-free regime. This results in two new black-box adversarial attack generation methods, ZO-ADMM and BO-ADMM. Our empirical evaluations on image classification datasets show that our proposed approaches have much lower function query complexities compared to state-of-the-art attack methods, but achieve very competitive attack success rates.

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"
    - "fault sneaking attack a stealthy framework for misleading deep neural networks"
    - "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 "defending against backdoor attack on deep neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "req yolo a resource aware efficient quantization framework for object detection on fpgas"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "is feature selection secure against training data poisoning"
    - "spectral signatures in backdoor attacks"

Connected through hyperedge HE-3 (induced by source paper "towards an efficient and general framework of robust training for graph neural networks"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "zo adamm zeroth order adaptive momentum method for black box optimization"
    - "adversarial attacks on graph neural networks via meta learning"
    - "certifiable robustness and robust training for graph convolutional networks"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "adversarial attack on graph structured data"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "universal decision based black box perturbations breaking security through obscurity defenses"

Connected through hyperedge HE-4 (induced by source paper "taking care of the discretization problem a black box adversarial image attack in discrete integer domain"):
  with members:
    - "towards improved testing for deep learning"
    - "one pixel attack for fooling deep neural networks"
    - "procedural noise adversarial examples for black box attacks on deep convolutional networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "spatially transformed adversarial examples"
    - "formal security analysis of neural networks using symbolic intervals"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "a dual approach to scalable verification of deep networks"

Connected through hyperedge HE-5 (induced by source paper "light weight calibrator a separable component for unsupervised domain adaptation"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "do imagenet classifiers generalize to imagenet"
    - "a fourier perspective on model robustness in computer vision"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "structured adversarial attack towards general implementation and better interpretability"
    - "cycada cycle consistent adversarial domain adaptation"

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.CV(Computer Vision and Pattern Recognition)