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: "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.

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 examples versus cloud based detectors a black box empirical study"
    - "adversarial t shirt evading person detectors in a physical world"

Connected through hyperedge HE-2 (induced by source paper "dropcluster a structured dropout for convolutional networks"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "confidence calibration for convolutional neural networks using structured dropout"
    - "pytorch an imperative style high performance deep learning library"
    - "dropblock a regularization method for convolutional networks"
    - "open category detection with pac guarantees"
    - "feature grouping as a stochastic regularizer for high dimensional structured data"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "deep anomaly detection with outlier exposure"

Connected through hyperedge HE-3 (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"
    - "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 "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"
    - "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-5 (induced by source paper "bridging the gap between computational photography and visual recognition"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "isaid a large scale dataset for instance segmentation in aerial images"
    - "report on ug 2 challenge track 1 assessing algorithms to improve video object detection and classification from unconstrained mobility platforms"
    - "single image deraining a comprehensive benchmark analysis"
    - "dual directed capsule network for very low resolution image recognition"
    - "vision meets drones a challenge"
    - "the perception distortion tradeoff"
    - "crafting a toolchain for image restoration by deep reinforcement learning"

Connected through hyperedge HE-6 (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"
    - "query efficient black box adversarial examples superceded"
    - "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-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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "object recognition under multifarious conditions a reliability analysis and a feature similarity based performance estimation"):
  with members:
    - "challenging environments for traffic sign detection reliability assessment under inclement conditions"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "traffic signs in the wild highlights from the ieee video and image processing cup 2017 student competition sp competitions"
    - "cure or challenging unreal and real environments for object recognition"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "cure tsr challenging unreal and real environments for traffic sign recognition"
    - "on classification of distorted images with deep convolutional neural networks"

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)