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: "an evaluation of popular copy move forgery detection approaches"
  Abstract: A copy-move forgery is created by copying and pasting content within the same image, and potentially postprocessing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features Sift and Surf, as well as the block-based DCT, DWT, KPCA, PCA, and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.

Connected through hyperedge HE-1 (induced by source paper "r fakeddit a new multimodal benchmark dataset for fine grained fake news detection"):
  with members:
    - "nela gt 2018 a large multi labelled news dataset for the study of misinformation in news articles"
    - "defending against neural fake news"
    - "vatex a large scale high quality multilingual dataset for video and language research"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "deep contextualized word representations"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (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"
    - "camera based image forgery localization using convolutional neural networks"

Connected through hyperedge HE-3 (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"
    - "detection and localization of image forgeries using resampling features and deep learning"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "copy move source target disambiguation through multi branch cnns"):
  with members:
    - "fighting fake news image splice detection via learned self consistency"
    - "noiseprint a cnn based camera model fingerprint"
    - "copy move forgery detection based on convolutional kernel network"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-5 (induced by source paper "bilinear faster rcnn with ela for image tampering detection"):
  with members:
    - "learning rich features for image manipulation detection"
    - "an evaluation of digital image forgery detection approaches"
    - "bilinear cnn models for fine grained visual recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "robust copy move forgery detection by false alarms control"):
  with members:
    - "reducing anomaly detection in images to detection in noise"
    - "fighting fake news image splice detection via learned self consistency"

Connected through hyperedge HE-7 (induced by source paper "adversarial learning for image forensics deep matching with atrous convolution"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "adversarial learning for semi supervised semantic segmentation"
    - "deep matching and validation network an end to end solution to constrained image splicing localization and detection"
    - "copy move forgery detection based on convolutional kernel network"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deep image homography estimation"
    - "fully trainable deep matching"
    - "semantic segmentation using adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "a coarse to fine deep convolutional neural network framework for frame duplication detection and localization in video forgery"):
  with members:
    - "domain adaptive faster r cnn for object detection in the wild"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "convolutional image captioning"
    - "a patchmatch based dense field algorithm for video copy move detection and localization"
    - "convnets and imagenet beyond accuracy understanding mistakes and uncovering biases"
    - "a survey on video forgery detection"

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)