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: "adversarial examples versus cloud based detectors a black box empirical study"
  Abstract: Deep learning has been broadly leveraged by major cloud providers, such as Google, AWS and Baidu, to offer various computer vision related services including image classification, object identification, illegal image detection, etc. While recent works extensively demonstrated that deep learning classification models are vulnerable to adversarial examples, cloud-based image detection models, which are more complicated than classifiers, may also have similar security concern but not get enough attention yet. In this paper, we mainly focus on the security issues of real-world cloud-based image detectors. Specifically, (1) based on effective semantic segmentation, we propose four attacks to generate semantics-aware adversarial examples via only interacting with black-box APIs; and (2) we make the first attempt to conduct an extensive empirical study of black-box attacks against real-world cloud-based image detectors. Through the comprehensive evaluations on five major cloud platforms: AWS, Azure, Google Cloud, Baidu Cloud, and Alibaba Cloud, we demonstrate that our image processing based attacks can reach a success rate of approximately 100%, and the semantic segmentation based attacks have a success rate over 90% among different detection services, such as violence, politician, and pornography detection. We also proposed several possible defense strategies for these security challenges in the real-life situation.

Connected through hyperedge HE-1 (induced by source paper "advbox a toolbox to generate adversarial examples that fool neural networks"):
  with members:
    - "fastwordbug a fast method to generate adversarial text against nlp applications"
    - "transferability of adversarial examples to attack cloud based image classifier service"
    - "robust audio adversarial example for a physical attack"
    - "daedalus breaking non maximum suppression in object detection via adversarial examples"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "a tour of convolutional networks guided by linear interpreters"
    - "adversarial t shirt evading person detectors in a physical world"
    - "textbugger generating adversarial text against real world applications"

Connected through hyperedge HE-2 (induced by source paper "cloud based image classification service is not robust to adversarial examples a forgotten battlefield"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "google s cloud vision api is not robust to noise"
    - "query efficient black box adversarial examples superceded"
    - "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 "transferability of adversarial examples to attack cloud based image classifier service"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "google s cloud vision api is not robust to noise"
    - "query efficient black box adversarial examples superceded"
    - "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-4 (induced by source paper "ai powered gui attack and its defensive methods"):
  with members:
    - "black box adversarial attacks with bayesian optimization"
    - "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-5 (induced by source paper "adversarial examples opportunities and challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"
    - "hcic hardware assisted control flow integrity checking"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "adversarial attacks against medical deep learning systems"
    - "targeted adversarial examples for black box audio systems"
    - "adversarial patch"

Connected through hyperedge HE-6 (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"
    - "spatially transformed adversarial examples"
    - "exploring the landscape of spatial robustness"
    - "google s cloud vision api is not robust to noise"
    - "query efficient black box adversarial examples superceded"
    - "ssd single shot multibox detector"
    - "delving into transferable adversarial examples and black box attacks"
    - "membership inference attacks against machine learning models"

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)