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 "adversarial attacks beyond the image space"):
  Abstract: Generating adversarial examples is an intriguing problem and an important way of understanding the working mechanism of deep neural networks. Recently, it has attracted a lot of attention in the computer vision community. Most existing approaches generated perturbations in image space, i.e., each pixel can be modified independently. However, it remains unclear whether these adversarial examples are authentic, in the sense that they correspond to actual changes in physical properties. #R##N#This paper aims at exploring this topic in the contexts of object classification and visual question answering. The baselines are set to be several state-of-the-art deep neural networks which receive 2D input images. We augment these networks with a differentiable 3D rendering layer in front, so that a 3D scene (in physical space) is rendered into a 2D image (in image space), and then mapped to a prediction (in output space). There are two (direct or indirect) ways of attacking the physical parameters. The former back-propagates the gradients of error signals from output space to physical space directly, while the latter first constructs an adversary in image space, and then attempts to find the best solution in physical space that is rendered into this image. An important finding is that attacking physical space is much more difficult, as the direct method, compared with that used in image space, produces a much lower success rate and requires heavier perturbations to be added. On the other hand, the indirect method does not work out, suggesting that adversaries generated in image space are inauthentic. By interpreting them in physical space, most of these adversaries can be filtered out, showing promise in defending adversaries.

Includes members:
  Member "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense")
  Member "robust physical world attacks on deep learning models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  Member "inferring and executing programs for visual reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised question decomposition for question answering")
  Member "exploring the landscape of spatial robustness":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "towards practical verification of machine learning the case of computer vision systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "certification of semantic perturbations via randomized smoothing")
  Member "material editing using a physically based rendering network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a method for estimating reflectance map and material using deep learning with synthetic dataset")
  Member "synthesizing robust adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")
  Member "adversarial examples for semantic segmentation and object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")

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)