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: "black box adversarial attacks with bayesian optimization"
  Abstract: We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output pairs. We use Bayesian optimization~(BO) to specifically cater to scenarios involving low query budgets to develop query efficient adversarial attacks. We alleviate the issues surrounding BO in regards to optimizing high dimensional deep learning models by effective dimension upsampling techniques. Our proposed approach achieves performance comparable to the state of the art black-box adversarial attacks albeit with a much lower average query count. In particular, in low query budget regimes, our proposed method reduces the query count up to $80\%$ with respect to the state of the art methods.

Connected through hyperedge HE-1 (induced by source paper "vec2face unveil human faces from their blackbox features in face recognition"):
  with members:
    - "shrinkteanet million scale lightweight face recognition via shrinking teacher student networks"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning towards minimum hyperspherical energy"
    - "black box adversarial attacks with limited queries and information"
    - "constructing unrestricted adversarial examples with generative models"
    - "mobiface a lightweight deep learning face recognition on mobile devices"

Connected through hyperedge HE-2 (induced by source paper "ai powered gui attack and its defensive methods"):
  with members:
    - "adversarial examples versus cloud based detectors a black box empirical study"
    - "using ai to hack ia a new stealthy spyware against voice assistance functions in smart phones"
    - "the elephant in the room"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "a review paper noise models in digital image processing"
    - "distillation as a defense to adversarial perturbations against deep neural networks"

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"
    - "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"
    - "hybrid batch attacks finding black box adversarial examples with limited queries"

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)