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: "detecting gan generated fake images using co occurrence matrices"
  Abstract: The advent of Generative Adversarial Networks (GANs) has brought about completely novel ways of transforming and manipulating pixels in digital images. GAN based techniques such as Image-to-Image translations, DeepFakes, and other automated methods have become increasingly popular in creating fake images. In this paper, we propose a novel approach to detect GAN generated fake images using a combination of co-occurrence matrices and deep learning. We extract co-occurrence matrices on three color channels in the pixel domain and train a model using a deep convolutional neural network (CNN) framework. Experimental results on two diverse and challenging GAN datasets comprising more than 56,000 images based on unpaired image-to-image translations (cycleGAN [1]) and facial attributes/expressions (StarGAN [2]) show that our approach is promising and achieves more than 99% classification accuracy in both datasets. Further, our approach also generalizes well and achieves good results when trained on one dataset and tested on the other.

Connected through hyperedge HE-1 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "the deepfake detection challenge dfdc preview dataset"
    - "analyzing and improving the image quality of stylegan"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "detecting and simulating artifacts in gan fake images"
    - "celeb df a new dataset for deepfake forensics"

Connected through hyperedge HE-2 (induced by source paper "global texture enhancement for fake face detection in the wild"):
  with members:
    - "on the generalization of gan image forensics"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "exposing gan synthesized faces using landmark locations"
    - "detecting and simulating artifacts in gan fake images"
    - "incremental learning for the detection and classification of gan generated images"
    - "fakespotter a simple yet robust baseline for spotting ai synthesized fake faces"
    - "do gans leave artificial fingerprints"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"

Connected through hyperedge HE-3 (induced by source paper "discover your social identity from what you tweet a content based approach"):
  with members:
    - "exploiting multi domain visual information for fake news detection"
    - "a hierarchical location prediction neural network for twitter user geolocation"
    - "fact checking meets fauxtography verifying claims about images"
    - "false news detection on social media"
    - "aird adversarial learning framework for image repurposing detection"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "exposing deep fakes using inconsistent head poses"
    - "exposing deepfake videos by detecting face warping artifacts"

Connected through hyperedge HE-4 (induced by source paper "fakespotter a simple yet robust baseline for spotting ai synthesized fake faces"):
  with members:
    - "deepfakes and beyond a survey of face manipulation and fake detection"
    - "faceforensics learning to detect manipulated facial images"
    - "analyzing and improving the image quality of stylegan"
    - "few shot adversarial learning of realistic neural talking head models"
    - "detecting and simulating artifacts in gan fake images"
    - "celeb df a new dataset for deepfake forensics"
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "on the detection of digital face manipulation"

Connected through hyperedge HE-5 (induced by source paper "cnn generated images are surprisingly easy to spot for now"):
  with members:
    - "seeing what a gan cannot generate"
    - "faceforensics learning to detect manipulated facial images"
    - "analyzing and improving the image quality of stylegan"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "detecting and simulating artifacts in gan fake images"
    - "fakespotter a simple yet robust baseline for spotting ai synthesized fake faces"
    - "semantic image synthesis with spatially adaptive normalization"
    - "learning rich features for image manipulation detection"

Connected through hyperedge HE-6 (induced by source paper "real or fake spoofing state of the art face synthesis detection systems"):
  with members:
    - "biometric presentation attack detection beyond the visible spectrum"
    - "faceforensics learning to detect manipulated facial images"
    - "the deepfake detection challenge dfdc preview dataset"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "detecting and simulating artifacts in gan fake images"
    - "fakespotter a simple yet robust baseline for spotting ai synthesized fake faces"
    - "on the detection of digital face manipulation"
    - "interpreting the latent space of gans for semantic face editing"

Connected through hyperedge HE-7 (induced by source paper "detecting and simulating artifacts in gan fake images"):
  with members:
    - "source generator attribution via inversion"
    - "semantic image synthesis with spatially adaptive normalization"
    - "do gans leave artificial fingerprints"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "attributing fake images to gans analyzing fingerprints in generated images"
    - "detecting gan generated imagery using color cues"
    - "large scale gan training for high fidelity natural image synthesis"
    - "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)