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: "image splicing localization using a multi task fully convolutional network mfcn"
  Abstract: Abstract   In this work, we propose a technique that utilizes a fully convolutional network (FCN) to localize image splicing attacks. We first evaluated a single-task FCN (SFCN) trained only on the surface label. Although the SFCN is shown to provide superior performance over existing methods, it still provides a coarse localization output in certain cases. Therefore, we propose the use of a multi-task FCN (MFCN) that utilizes two output branches for multi-task learning. One branch is used to learn the surface label, while the other branch is used to learn the edge or boundary of the spliced region. We trained the networks using the CASIA v2.0 dataset, and tested the trained models on the CASIA v1.0, Columbia Uncompressed, Carvalho, and the DARPA/NIST Nimble Challenge 2016 SCI datasets. Experiments show that the SFCN and MFCN outperform existing splicing localization algorithms, and that the MFCN can achieve finer localization than the SFCN.

Connected through hyperedge HE-1 (induced by source paper "object discovery with a copy pasting gan"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "inserting videos into videos"
    - "monet unsupervised scene decomposition and representation"
    - "unsupervised moving object detection via contextual information separation"
    - "multi object representation learning with iterative variational inference"
    - "learning to generate synthetic data via compositing"
    - "unsupervised image matching and object discovery as optimization"
    - "learning to segment via cut and paste"

Connected through hyperedge HE-2 (induced by source paper "face x ray for more general face forgery detection"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "on the generalization of gan image forensics"
    - "hybrid lstm and encoder decoder architecture for detection of image forgeries"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "celeb df a new dataset for deepfake forensics"
    - "incremental learning for the detection and classification of gan generated images"

Connected through hyperedge HE-3 (induced by source paper "perceptually motivated method for image inpainting comparison"):
  with members:
    - "learning rich features for image manipulation detection"
    - "spectral normalization for generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "shift net image inpainting via deep feature rearrangement"
    - "generative image inpainting with contextual attention"
    - "progressive neural architecture search"
    - "image inpainting using multi scale feature image translation"
    - "deep image prior"

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"
    - "camera based image forgery localization using convolutional neural networks"
    - "boundary based image forgery detection by fast shallow cnn"

Connected through hyperedge HE-5 (induced by source paper "spliceradar a learned method for blind image forensics"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "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"
    - "recasting residual based local descriptors as convolutional neural networks an application to image forgery detection"
    - "first steps toward camera model identification with convolutional neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "efficient image splicing localization via contrastive feature extraction"):
  with members:
    - "learning rich features for image manipulation detection"
    - "fighting fake news image splice detection via learned self consistency"
    - "noiseprint a cnn based camera model fingerprint"
    - "dpca dimensionality reduction for discriminative analytics of multiple large scale datasets"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (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"
    - "cbam convolutional block attention module"
    - "detection and localization of image forgeries using resampling features and deep learning"
    - "mask r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-8 (induced by source paper "forensictransfer weakly supervised domain adaptation for forgery detection"):
  with members:
    - "detecting and simulating artifacts in gan fake images"
    - "a closer look at few shot classification"
    - "learning rich features for image manipulation detection"
    - "fighting fake news image splice detection via learned self consistency"
    - "mesonet a compact facial video forgery detection network"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "noiseprint a cnn based camera model fingerprint"
    - "a style based generator architecture for generative adversarial networks"

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)