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: "adversarial framing for image and video classification"
  Abstract: Neural networks are prone to adversarial attacks. In general, such attacks deteriorate the quality of the input by either slightly modifying most of its pixels, or by occluding it with a patch. In this paper, we propose a method that keeps the image unchanged and only adds an adversarial framing on the border of the image. We show empirically that our method is able to successfully attack state-of-the-art methods on both image and video classification problems. Notably, the proposed method results in a universal attack which is very fast at test time. Source code can be found at this https URL .

Connected through hyperedge HE-1 (induced by source paper "identifying and resisting adversarial videos using temporal consistency"):
  with members:
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "sparse adversarial perturbations for videos"
    - "max mahalanobis linear discriminant analysis networks"
    - "adversarial perturbations against real time video classification systems"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "countering adversarial images using input transformations"
    - "mitigating adversarial effects through randomization"

Connected through hyperedge HE-2 (induced by source paper "robustness verification of support vector machines"):
  with members:
    - "optimization and abstraction a synergistic approach for analyzing neural network robustness"
    - "formal security analysis of neural networks using symbolic intervals"
    - "formal verification of piece wise linear feed forward neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "generating natural adversarial examples"
    - "safety verification of deep neural networks"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-3 (induced by source paper "towards hiding adversarial examples from network interpretation"):
  with members:
    - "fooling neural network interpretations via adversarial model manipulation"
    - "adversarial patch"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "lavan localized and visible adversarial noise"
    - "sanity checks for saliency maps"
    - "tell me where to look guided attention inference network"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "darts deceiving autonomous cars with toxic signs"

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)