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 "amora black box adversarial morphing attack"):
  Abstract: Nowadays, digital facial content manipulation has become ubiquitous and realistic with the unprecedented success of generative adversarial networks (GANs) in image synthesis. Unfortunately, face recognition (FR) systems suffer from severe security concerns due to facial image manipulations. In this paper, we investigate and introduce a new type of adversarial attack to evade FR systems by manipulating facial content, called adversarial morphing attack (a.k.a. Amora). In contrast to adversarial noise attack that perturbs pixel intensity values by adding human-imperceptible noise, our proposed adversarial morphing attack is a semantic attack that perturbs pixels spatially in a coherent manner. To tackle the black-box attack problem, we have devised a simple yet effective learning pipeline to obtain a proprietary optical flow field for each attack. We have quantitatively and qualitatively demonstrated the effectiveness of our adversarial morphing attack at various levels of morphing intensity on two popular FR systems with smiling facial expression manipulations. Experimental results indicate that a novel black-box adversarial attack based on local deformation is possible, which is vastly different from additive noise based attacks. The findings of this work may pave a new research direction towards a more thorough understanding and investigation of image-based adversarial attacks and defenses.

Includes members:
  Member "fakelocator robust localization of gan based face manipulations via semantic segmentation networks with bells and whistles":
    also connected through other hyperedges:
      (none)
  Member "advfaces adversarial face synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the detection of digital face manipulation")
  Member "efficient decision based black box adversarial attacks on face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "toward adversarial robustness via semi supervised robust training")
  Member "attacking optical flow":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analysis of explainers of black box deep neural networks for computer vision a survey")
  Member "fakespotter a simple baseline for spotting ai synthesized fake faces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "wasserstein adversarial examples via projected sinkhorn iterations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "wasserstein smoothing certified robustness against wasserstein adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "randomized smoothing of all shapes and sizes")
  Member "machine learning testing survey landscapes and horizons":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "importance driven deep learning system testing")

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)