human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "black box adversarial attack with transferable model based embedding"):
  Abstract: We present a new method for black-box adversarial attack. Unlike previous methods that combined transfer-based and scored-based methods by using the gradient or initialization of a surrogate white-box model, this new method tries to learn a low-dimensional embedding using a pretrained model, and then performs efficient search within the embedding space to attack an unknown target network. The method produces adversarial perturbations with high level semantic patterns that are easily transferable. We show that this approach can greatly improve the query efficiency of black-box adversarial attack across different target network architectures. We evaluate our approach on MNIST, ImageNet and Google Cloud Vision API, resulting in a significant reduction on the number of queries. We also attack adversarially defended networks on CIFAR10 and ImageNet, where our method not only reduces the number of queries, but also improves the attack success rate.

Includes members:
  Member "parsimonious black box adversarial attacks via efficient combinatorial optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trust but verify an information theoretic explanation for the adversarial fragility of machine learning systems and a general defense against adversarial attacks")
  Member "efficient decision based black box adversarial attacks on face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "toward adversarial robustness via semi supervised robust training")
  Member "a direct approach to robust deep learning using adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial distributional training for robust deep learning")
  Member "simple black box adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "black box smoothing a provable defense for pretrained classifiers")
  Member "improving black box adversarial attacks with a transfer based prior":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "geoda a geometric framework for black box adversarial attacks")
  Member "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey of black box adversarial attacks on computer vision models")
  Member "adversarial vision challenge":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "random smoothing might be unable to certify ell_ infty robustness for high dimensional images")

Question: Please tell me which class the source paper 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)