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: "first steps toward camera model identification with convolutional neural networks"
  Abstract: Detecting the camera model used to shoot a picture enables to solve a wide series of forensic problems, from copyright infringement to ownership attribution. For this reason, the forensic community has developed a set of camera model identification algorithms that exploit characteristic traces left on acquired images by the processing pipelines specific of each camera model. In this letter, we investigate a novel approach to solve camera model identification problem. Specifically, we propose a data-driven algorithm based on convolutional neural networks, which learns features characterizing each camera model directly from the acquired pictures. Results on a well-known dataset of 18 camera models show that: 1) the proposed method outperforms up-to-date state-of-the-art algorithms on classification of 64 × 64 color image patches; 2) features learned by the proposed network generalize to camera models never used for training.

Connected through hyperedge HE-1 (induced by source paper "cnn based fast source device identification"):
  with members:
    - "spn cnn boosting sensor based source camera attribution with deep learning"
    - "forensic similarity for digital images"
    - "fighting fake news image splice detection via learned self consistency"
    - "accurate and scalable image clustering based on sparse representation of camera fingerprint"
    - "noiseprint a cnn based camera model fingerprint"
    - "facing device attribution problem for stabilized video sequences"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "forensic similarity for digital images"):
  with members:
    - "fighting fake news image splice detection via learned self consistency"
    - "noiseprint a cnn based camera model fingerprint"
    - "reliability map estimation for cnn based camera model attribution"
    - "camera based image forgery localization using convolutional neural networks"
    - "detection and localization of image forgeries using resampling features and deep learning"
    - "aligned and non aligned double jpeg detection using convolutional neural networks"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-3 (induced by source paper "forensic scanner identification using machine learning"):
  with members:
    - "xception deep learning with depthwise separable convolutions"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "learning deep features for discriminative localization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (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"
    - "a counter forensic method for cnn based camera model identification"

Connected through hyperedge HE-5 (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-6 (induced by source paper "neural imaging pipelines the scourge or hope of forensics"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "stereoscopic dark flash for low light photography"
    - "cnn based adversarial embedding for image steganography"
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "learning rich features for image manipulation detection"
    - "synthetic depth of field with a single camera mobile phone"
    - "do gans leave artificial fingerprints"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"

Connected through hyperedge HE-7 (induced by source paper "controversial stimuli pitting neural networks against each other as models of human recognition"):
  with members:
    - "mnist c a robustness benchmark for computer vision"
    - "conditional generative models are not robust"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "detecting and diagnosing adversarial images with class conditional capsule reconstructions"
    - "humans can decipher adversarial images"
    - "deep predictive coding network for object recognition"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "towards the first adversarially robust neural network model on mnist"

Connected through hyperedge HE-8 (induced by source paper "detecting colorized images via convolutional neural networks toward high accuracy and good generalization"):
  with members:
    - "fake colorized image detection"
    - "detection and localization of image forgeries using resampling features and deep learning"
    - "aligned and non aligned double jpeg detection using convolutional neural networks"
    - "learning representations for automatic colorization"
    - "aggregated residual transformations for deep neural networks"
    - "densely connected convolutional networks"
    - "colorful image colorization"
    - "understanding neural networks through deep visualization"

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)