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: "query efficient meta attack to deep neural networks"
  Abstract: Recently, several adversarial attack methods to black-box deep neural networks have been proposed and they serve as an excellent testing bed for investigating safety issues with DNNs. These methods generally take in the query and corresponding feedback from the targeted DNN model and infer suitable attack patterns accordingly. However, due to lacking prior and inefficiency in leveraging the query information, these methods are mostly query-intensive. In this work, we propose a meta attack strategy which is capable of attacking the target black-box model with much fewer queries. Its high query-efficiency comes from prior abstraction on training a meta attacker which can speed up the search for adversarial examples significantly. Extensive experiments on MNIST, CIFAR10 and tiny-Imagenet demonstrate that, our meta-attack method can remarkably reduce the number of model queries without sacrificing the attack performance. Moreover, the obtained meta attacker is not restricted to a particular model but can be reused easily with fast adaptive ability to attack a variety of models.

Connected through hyperedge HE-1 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "image super resolution as a defense against adversarial attacks"
    - "explaining convolutional neural networks using softmax gradient layer wise relevance propagation"
    - "yet another but more efficient black box adversarial attack tiling and evolution strategies"
    - "natural adversarial examples"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "interpreting adversarially trained convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "damagenet a universal adversarial dataset"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "yet another but more efficient black box adversarial attack tiling and evolution strategies"
    - "natural adversarial examples"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "improving black box adversarial attacks with a transfer based prior"
    - "once a man towards multi target attack via learning multi target adversarial network once"

Connected through hyperedge HE-3 (induced by source paper "square attack a query efficient black box adversarial attack via random search"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "one pixel attack for fooling deep neural networks"
    - "sparse and imperceivable adversarial attacks"
    - "black box adversarial attacks with bayesian optimization"
    - "certified adversarial robustness via randomized smoothing"
    - "hopskipjumpattack a query efficient decision based attack"
    - "yet another but more efficient black box adversarial attack tiling and evolution strategies"
    - "there are no bit parts for sign bits in black box attacks"

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)