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Center node: "query efficient black box adversarial examples superceded"
  Abstract: Note that this paper is superceded by "Black-Box Adversarial Attacks with Limited Queries and Information." #R##N#Current neural network-based image classifiers are susceptible to adversarial examples, even in the black-box setting, where the attacker is limited to query access without access to gradients. Previous methods --- substitute networks and coordinate-based finite-difference methods --- are either unreliable or query-inefficient, making these methods impractical for certain problems. #R##N#We introduce a new method for reliably generating adversarial examples under more restricted, practical black-box threat models. First, we apply natural evolution strategies to perform black-box attacks using two to three orders of magnitude fewer queries than previous methods. Second, we introduce a new algorithm to perform targeted adversarial attacks in the partial-information setting, where the attacker only has access to a limited number of target classes. Using these techniques, we successfully perform the first targeted adversarial attack against a commercially deployed machine learning system, the Google Cloud Vision API, in the partial information setting.

Connected through hyperedge HE-1 (induced by source paper "cloud based image classification service is not robust to adversarial examples a forgotten battlefield"):
  with members:
    - "adversarial examples versus cloud based detectors a black box empirical study"
    - "an analysis of deep neural network models for practical applications"
    - "google s cloud vision api is not robust to noise"
    - "mitigating adversarial effects through randomization"
    - "practical black box attacks against machine learning"
    - "ssd single shot multibox detector"
    - "delving into transferable adversarial examples and black box attacks"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-2 (induced by source paper "transferability of adversarial examples to attack cloud based image classifier service"):
  with members:
    - "adversarial examples versus cloud based detectors a black box empirical study"
    - "an analysis of deep neural network models for practical applications"
    - "google s cloud vision api is not robust to noise"
    - "mitigating adversarial effects through randomization"
    - "practical black box attacks against machine learning"
    - "ssd single shot multibox detector"
    - "delving into transferable adversarial examples and black box attacks"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-3 (induced by source paper "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "mesonet a compact facial video forgery detection network"
    - "exposing deep fakes using inconsistent head poses"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "pyramidbox a context assisted single shot face detector"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"

Connected through hyperedge HE-4 (induced by source paper "adversarial examples versus cloud based detectors a black box empirical study"):
  with members:
    - "exploring the space of black box attacks on deep neural networks"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "learning universal adversarial perturbations with generative models"
    - "google s cloud vision api is not robust to noise"
    - "generic black box end to end attack against state of the art api call based malware classifiers"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "robust decision trees against adversarial examples"):
  with members:
    - "gradient regularized budgeted boosting"
    - "theoretically principled trade off between robustness and accuracy"
    - "multi layered gradient boosting decision trees"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "scaling provable adversarial defenses"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "mixtrain scalable training of formally robust neural networks"

Connected through hyperedge HE-6 (induced by source paper "a geometry inspired decision based attack"):
  with members:
    - "query efficient black box attack by active learning"
    - "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"
    - "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-7 (induced by source paper "cloud based image classification service is not robust to simple transformations a forgotten battlefield"):
  with members:
    - "transferability of adversarial examples to attack cloud based image classifier service"
    - "adversarial examples versus cloud based detectors a black box empirical study"
    - "spatially transformed adversarial examples"
    - "exploring the landscape of spatial robustness"
    - "google s cloud vision api is not robust to noise"
    - "ssd single shot multibox detector"
    - "delving into transferable adversarial examples and black box attacks"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-8 (induced by source paper "built in vulnerabilities to imperceptible adversarial perturbations"):
  with members:
    - "adversarial training versus weight decay"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial spheres"
    - "adversarially robust generalization requires more data"
    - "sok security and privacy in machine learning"
    - "audio adversarial examples targeted attacks on speech to text"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"

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)