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: "noiseprint a cnn based camera model fingerprint"
  Abstract: Forensic analyses of digital images rely heavily on the traces of in-camera and out-camera processes left on the acquired images. Such traces represent a sort of camera fingerprint. If one is able to recover them, by suppressing the high-level scene content and other disturbances, a number of forensic tasks can be easily accomplished. A notable example is the PRNU pattern, which can be regarded as a device fingerprint, and has received great attention in multimedia forensics. In this paper we propose a method to extract a camera model fingerprint, called noiseprint, where the scene content is largely suppressed and model-related artifacts are enhanced. This is obtained by means of a Siamese network, which is trained with pairs of image patches coming from the same (label +1) or different (label -1) cameras. Although noiseprints can be used for a large variety of forensic tasks, here we focus on image forgery localization. Experiments on several datasets widespread in the forensic community show noiseprint-based methods to provide state-of-the-art performance.

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"
    - "facing device attribution problem for stabilized video sequences"
    - "first steps toward camera model identification with convolutional neural networks"
    - "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"
    - "reliability map estimation for cnn based camera model attribution"
    - "camera based image forgery localization using convolutional neural networks"
    - "first steps toward camera model identification with 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 "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"
    - "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"
    - "a counter forensic method for cnn based camera model identification"

Connected through hyperedge HE-4 (induced by source paper "spn cnn boosting sensor based source camera attribution with deep learning"):
  with members:
    - "facing device attribution problem for stabilized video sequences"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-5 (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-6 (induced by source paper "ct gan malicious tampering of 3d medical imagery using deep learning"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "generative adversarial network in medical imaging a review"
    - "fighting fake news image splice detection via learned self consistency"
    - "melanogans high resolution skin lesion synthesis with gans"
    - "cross modality synthesis from ct to pet using fcn and gan networks for improved automated lesion detection"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "learning implicit brain mri manifolds with deep learning"
    - "blood vessel geometry synthesis using generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "scalable fine grained generated image classification based on deep metric learning"):
  with members:
    - "learning rich features for image manipulation detection"
    - "universal image manipulation detection using deep siamese convolutional neural network"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "do gans leave artificial fingerprints"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "learning to detect fake face images in the wild"
    - "a style based generator architecture for generative adversarial networks"
    - "detection of deep network generated images using disparities in color components"

Connected through hyperedge HE-8 (induced by source paper "unmasking deepfakes with simple features"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "attributing fake images to gans learning and analyzing gan fingerprints"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "mesonet a compact facial video forgery detection network"
    - "exposing deep fakes using inconsistent head poses"
    - "learning to detect fake face images in the wild"
    - "detecting gan generated imagery using color cues"
    - "large scale gan training for high fidelity natural image synthesis"

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)