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: "a counter forensic method for cnn based camera model identification"
  Abstract: An increasing number of digital images are being shared and accessed through websites, media, and social applications. Many of these images have been modified and are not authentic. Recent advances in the use of deep convolutional neural networks (CNNs) have facilitated the task of analyzing the veracity and authenticity of largely distributed image datasets. We examine in this paper the problem of identifying the camera model or type that was used to take an image and that can be spoofed. Due to the linear nature of CNNs and the high-dimensionality of images, neural networks are vulnerable to attacks with adversarial examples. These examples are imperceptibly different from correctly classified images but are misclassified with high confidence by CNNs. In this paper, we describe a counter-forensic method capable of subtly altering images to change their estimated camera model when they are analyzed by any CNN-based camera model detector. Our method can use both the Fast Gradient Sign Method (FGSM) or the Jacobian-based Saliency Map Attack (JSMA) to craft these adversarial images and does not require direct access to the CNN. Our results show that even advanced deep learning architectures trained to analyze images and obtain camera model information are still vulnerable to our proposed method.

Connected through hyperedge HE-1 (induced by source paper "effectiveness of random deep feature selection for securing image manipulation detectors against adversarial examples"):
  with members:
    - "defending against adversarial attacks by randomized diversification"
    - "on the transferability of adversarial examples against cnn based image forensics"
    - "secure detection of image manipulation by means of random feature selection"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "mitigating adversarial effects through randomization"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-2 (induced by source paper "combining prnu and noiseprint for robust and efficient device source identification"):
  with members:
    - "accurate and scalable image clustering based on sparse representation of camera fingerprint"
    - "noiseprint a cnn based camera model fingerprint"
    - "deep learning for source camera identification on mobile devices"
    - "camera based image forgery localization using convolutional neural networks"
    - "first steps toward camera model identification with convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "spoc spoofing camera fingerprints"):
  with members:
    - "fsgan subject agnostic face swapping and reenactment"
    - "faceforensics learning to detect manipulated facial images"
    - "source generator attribution via inversion"
    - "advfaces adversarial face synthesis"
    - "at gan a generative attack model for adversarial transferring on generative adversarial nets"
    - "exposing gan synthesized faces using landmark locations"
    - "detecting and simulating artifacts in gan fake images"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"

Connected through hyperedge HE-4 (induced by source paper "on the transferability of adversarial examples against cnn based image forensics"):
  with members:
    - "deep learning for source camera identification on mobile devices"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "the limitations of deep learning in adversarial settings"
    - "intriguing properties of 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)