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: "detection and localization of image forgeries using resampling features and deep learning"
  Abstract: Resampling is an important signature of manipulated images. In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. In the first method, the Radon transform of resampling features are computed on overlapping image patches. Deep learning classifiers and a Gaussian conditional random field model are then used to create a heatmap. Tampered regions are located using a Random Walker segmentation method. In the second method, resampling features computed on overlapping image patches are passed through a Long short-term memory (LSTM) based network for classification and localization. We compare the performance of detection/localization of both these methods. Our experimental results show that both techniques are effective in detecting and localizing digital image forgeries.

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"
    - "camera based image forgery localization using convolutional neural networks"
    - "first steps toward camera model identification with convolutional neural networks"
    - "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 "detecting gan generated fake images using co occurrence matrices"):
  with members:
    - "on detecting gans and retouching based synthetic alterations"
    - "learning rich features for image manipulation detection"
    - "do gans leave artificial fingerprints"
    - "tequilagan how to easily identify gan samples"
    - "detecting gan generated imagery using color cues"
    - "detection of deep network generated images using disparities in color components"
    - "progressive growing of gans for improved quality stability and variation"
    - "generative face completion"

Connected through hyperedge HE-3 (induced by source paper "detecting colorized images via convolutional neural networks toward high accuracy and good generalization"):
  with members:
    - "fake colorized image detection"
    - "first steps toward camera model identification with convolutional neural networks"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "hybrid lstm and encoder decoder architecture for detection of image forgeries"):
  with members:
    - "learning rich features for image manipulation detection"
    - "scene classification based on multiscale convolutional neural network"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "constrained r cnn a general image manipulation detection model"):
  with members:
    - "hybrid lstm and encoder decoder architecture for detection of image forgeries"
    - "learning rich features for image manipulation detection"
    - "image splicing localization using a multi task fully convolutional network mfcn"
    - "cbam convolutional block attention module"
    - "mask r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "resampling detection of recompressed images via dual stream convolutional neural network"):
  with members:
    - "learning rich features for image manipulation detection"
    - "fighting fake news image splice detection via learned self consistency"
    - "image processing operations identification via convolutional neural network"

Connected through hyperedge HE-7 (induced by source paper "exposing deepfake videos by detecting face warping artifacts"):
  with members:
    - "recycle gan unsupervised video retargeting"
    - "mesonet a compact facial video forgery detection network"
    - "exposing deep fakes using inconsistent head poses"
    - "detecting gan generated imagery using color cues"
    - "noiseprint a cnn based camera model fingerprint"
    - "two stream neural networks for tampered face detection"
    - "detection of deep network generated images using disparities in color components"
    - "in ictu oculi exposing ai generated fake face videos by detecting eye blinking"

Connected through hyperedge HE-8 (induced by source paper "image splicing localization using a multi task fully convolutional network mfcn"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "fully convolutional networks for semantic segmentation"

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)