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: "sign opt a query efficient hard label adversarial attack"
  Abstract: We study the most practical problem setup for evaluating adversarial robustness of a machine learning system with limited access: the hard-label black-box attack setting for generating adversarial examples, where limited model queries are allowed and only the decision is provided to a queried data input. Several algorithms have been proposed for this problem but they typically require huge amount (>20,000) of queries for attacking one example. Among them, one of the state-of-the-art approaches (Cheng et al., 2019) showed that hard-label attack can be modeled as an optimization problem where the objective function can be evaluated by binary search with additional model queries, thereby a zeroth order optimization algorithm can be applied. In this paper, we adopt the same optimization formulation but propose to directly estimate the sign of gradient at any direction instead of the gradient itself, which enjoys the benefit of single query. Using this single query oracle for retrieving sign of directional derivative, we develop a novel query-efficient Sign-OPT approach for hard-label black-box attack. We provide a convergence analysis of the new algorithm and conduct experiments on several models on MNIST, CIFAR-10 and ImageNet. We find that Sign-OPT attack consistently requires 5X to 10X fewer queries when compared to the current state-of-the-art approaches, and usually converges to an adversarial example with smaller perturbation.

Connected through hyperedge HE-1 (induced by source paper "cat customized adversarial training for improved robustness"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "label smoothing and logit squeezing a replacement for adversarial training"
    - "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "certified adversarial robustness via randomized smoothing"
    - "convergence of adversarial training in overparametrized neural networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial robustness via adversarial label smoothing"

Connected through hyperedge HE-2 (induced by source paper "query efficient physical hard label attacks on deep learning visual classification"):
  with members:
    - "hopskipjumpattack a query efficient decision based attack"
    - "adversarial patch"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "black box adversarial attacks with limited queries and information"
    - "synthesizing robust adversarial examples"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "delving into transferable adversarial examples and black box attacks"

Connected through hyperedge HE-3 (induced by source paper "toward finding the global optimal of adversarial examples"):
  with members:
    - "hopskipjumpattack a query efficient decision based attack"
    - "robust decision trees against adversarial examples"
    - "enhancing gradient based attacks with symbolic intervals"
    - "ml loo detecting adversarial examples with feature attribution"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "mixtrain scalable training of formally robust neural networks"
    - "genattack practical black box attacks with gradient free optimization"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-4 (induced by source paper "bosh an efficient meta algorithm for decision based attacks"):
  with members:
    - "hopskipjumpattack a query efficient decision based attack"
    - "robust decision trees against adversarial examples"
    - "ml loo detecting adversarial examples with feature attribution"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "genattack practical black box attacks with gradient free optimization"
    - "black box adversarial attacks with limited queries and information"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "attacking visual language grounding with adversarial examples a case study on neural image captioning"

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)