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: "camera based image forgery localization using convolutional neural networks"
  Abstract: Camera fingerprints are precious tools for a number of image forensics tasks. A well-known example is the photo response non-uniformity (PRNU) noise pattern, a powerful device fingerprint. Here, to address the image forgery localization problem, we rely on noiseprint, a recently proposed CNN-based camera model fingerprint. The CNN is trained to minimize the distance between same-model patches, and maximize the distance otherwise. As a result, the noiseprint accounts for model-related artifacts just like the PRNU accounts for device-related non-uniformities. However, unlike the PRNU, it is only mildly affected by residuals of high-level scene content. The experiments show that the proposed noiseprint-based forgery localization method improves over the PRNU-based reference.

Connected through hyperedge HE-1 (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"
    - "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-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"
    - "first steps toward camera model identification with convolutional neural networks"
    - "a counter forensic method for cnn based camera model identification"

Connected through hyperedge HE-3 (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-4 (induced by source paper "a full image full resolution end to end trainable cnn framework for image forgery detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "hybrid lstm and encoder decoder architecture for detection of image forgeries"
    - "learning rich features for image manipulation detection"
    - "fighting fake news image splice detection via learned self consistency"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "noiseprint a cnn based camera model fingerprint"
    - "image splicing localization using a multi task fully convolutional network mfcn"
    - "boundary based image forgery detection by fast shallow cnn"

Connected through hyperedge HE-5 (induced by source paper "identity document and banknote security forensics a survey"):
  with members:
    - "midv 500 a dataset for identity document analysis and recognition on mobile devices in video stream"
    - "learning rich features for image manipulation detection"
    - "fighting fake news image splice detection via learned self consistency"
    - "noiseprint a cnn based camera model fingerprint"
    - "e counterfeit a mobile server platform for document counterfeit detection"
    - "humans are easily fooled by digital images"
    - "machine assisted authentication of paper currency an experiment on indian banknotes"

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)