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: "harmonic adversarial attack method"
  Abstract: Adversarial attacks find perturbations that can fool models into misclassifying images. Previous works had successes in generating noisy/edge-rich adversarial perturbations, at the cost of degradation of image quality. Such perturbations, even when they are small in scale, are usually easily spottable by human vision. In contrast, we propose Harmonic Adversar- ial Attack Methods (HAAM), that generates edge-free perturbations by using harmonic functions. The property of edge-free guarantees that the generated adversarial images can still preserve visual quality, even when perturbations are of large magnitudes. Experiments also show that adversaries generated by HAAM often have higher rates of success when transferring between models. In addition, we find harmonic perturbations can simulate natural phenomena like natural lighting and shadows. It would then be possible to help find corner cases for given models, as a first step to improving them.

Connected through hyperedge HE-1 (induced by source paper "attacking optical character recognition ocr systems with adversarial watermarks"):
  with members:
    - "smooth adversarial examples"
    - "adversarial patch"
    - "lavan localized and visible adversarial noise"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "on the statistical detection of adversarial examples"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-2 (induced by source paper "smooth adversarial examples"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "adversarial attacks and defences competition"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "a closed form solution to photorealistic image stylization"
    - "a flexible convolutional solver with application to photorealistic style transfer"
    - "low frequency adversarial perturbation"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "deep photo style transfer"

Connected through hyperedge HE-3 (induced by source paper "adversarial examples for edge detection they exist and they transfer"):
  with members:
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "generating natural language adversarial examples"
    - "intermediate level adversarial attack for enhanced transferability"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "countering adversarial images using input transformations"
    - "generative adversarial perturbations"
    - "adversarial examples for semantic segmentation and object detection"

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)