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 black box attack by active learning"
  Abstract: Deep neural network (DNN) as a popular machine learning model is found to be vulnerable to adversarial attack. This attack constructs adversarial examples by adding small perturbations to the raw input, while appearing unmodified to human eyes but will be misclassified by a well-trained classifier. In this paper, we focus on the black-box attack setting where attackers have almost no access to the underlying models. To conduct black-box attack, a popular approach aims to train a substitute model based on the information queried from the target DNN. The substitute model can then be attacked using existing white-box attack approaches, and the generated adversarial examples will be used to attack the target DNN. Despite its encouraging results, this approach suffers from poor query efficiency, i.e., attackers usually needs to query a huge amount of times to collect enough information for training an accurate substitute model. To this end, we first utilize state-of-the-art white-box attack methods to generate samples for querying, and then introduce an active learning strategy to significantly reduce the number of queries needed. Besides, we also propose a diversity criterion to avoid the sampling bias. Our extensive experimental results on MNIST and CIFAR-10 show that the proposed method can reduce more than $90\%$ of queries while preserve attacking success rates and obtain an accurate substitute model which is more than $85\%$ similar with the target oracle.

Connected through hyperedge HE-1 (induced by source paper "stealing black box functionality using the deep neural tree architecture"):
  with members:
    - "neural network gradient based learning of black box function interfaces"
    - "a survey on explainable artificial intelligence xai towards medical xai"
    - "knockoff nets stealing functionality of black box models"
    - "neural arithmetic logic units"
    - "hyperbolic neural networks"
    - "adaptive neural trees"
    - "stealing hyperparameters in machine learning"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-2 (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"
    - "prada protecting against dnn model stealing attacks"
    - "genattack practical black box attacks with gradient free optimization"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-3 (induced by source paper "a geometry inspired decision based attack"):
  with members:
    - "learning universal adversarial perturbations with generative models"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "query efficient black box adversarial examples superceded"
    - "simple black box adversarial perturbations for deep networks"
    - "practical black box attacks against machine learning"
    - "delving into transferable adversarial examples and black box attacks"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-4 (induced by source paper "extraction of complex dnn models real threat or boogeyman"):
  with members:
    - "high fidelity extraction of neural network models"
    - "deep neural network fingerprinting by conferrable adversarial examples"
    - "dawn dynamic adversarial watermarking of neural networks"
    - "do better imagenet models transfer better"
    - "knockoff nets stealing functionality of black box models"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"
    - "model extraction warning in mlaas paradigm"
    - "practical black box attacks against machine learning"

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)