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 "google s cloud vision api is not robust to noise"):
  Abstract: Google has recently introduced the Cloud Vision API for image analysis. According to the demonstration website, the API "quickly classifies images into thousands of categories, detects individual objects and faces within images, and finds and reads printed words contained within images." It can be also used to "detect different types of inappropriate content from adult to violent content." #R##N#In this paper, we evaluate the robustness of Google Cloud Vision API to input perturbation. In particular, we show that by adding sufficient noise to the image, the API generates completely different outputs for the noisy image, while a human observer would perceive its original content. We show that the attack is consistently successful, by performing extensive experiments on different image types, including natural images, images containing faces and images with texts. For instance, using images from ImageNet dataset, we found that adding an average of 14.25% impulse noise is enough to deceive the API. Our findings indicate the vulnerability of the API in adversarial environments. For example, an adversary can bypass an image filtering system by adding noise to inappropriate images. We then show that when a noise filter is applied on input images, the API generates mostly the same outputs for restored images as for original images. This observation suggests that cloud vision API can readily benefit from noise filtering, without the need for updating image analysis algorithms.

Includes members:
  Member "deceiving google s perspective api built for detecting toxic comments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wac a corpus of wikipedia conversations for online abuse detection")
  Member "blocking transferability of adversarial examples in black box learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey of black box adversarial attacks on computer vision models")
  Member "quality resilient deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise")
  Member "deceiving google s cloud video intelligence api built for summarizing videos":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "undersensitivity in neural reading comprehension")
  Member "dirty pixels optimizing image classification architectures for raw sensor data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "erase and restore simple accurate and resilient detection of l 2 adversarial examples")
  Member "examining the impact of blur on recognition by convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "benchmarking the robustness of semantic segmentation models")
  Member "practical black box attacks against machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")
  Member "how image degradations affect deep cnn based face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments")

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.CV(Computer Vision and Pattern Recognition)