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 imagery using color cues"
  Abstract: Image forensics is an increasingly relevant problem, as it can potentially address online disinformation campaigns and mitigate problematic aspects of social media. Of particular interest, given its recent successes, is the detection of imagery produced by Generative Adversarial Networks (GANs), e.g. `deepfakes'. Leveraging large training sets and extensive computing resources, recent work has shown that GANs can be trained to generate synthetic imagery which is (in some ways) indistinguishable from real imagery. We analyze the structure of the generating network of a popular GAN implementation, and show that the network's treatment of color is markedly different from a real camera in two ways. We further show that these two cues can be used to distinguish GAN-generated imagery from camera imagery, demonstrating effective discrimination between GAN imagery and real camera images used to train the GAN.

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"
    - "detecting gan generated fake images using co occurrence matrices"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "global texture enhancement for fake face detection in the wild"):
  with members:
    - "detecting gan generated fake images using co occurrence matrices"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "discover your social identity from what you tweet a content based approach"):
  with members:
    - "detecting gan generated fake images using co occurrence matrices"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "leveraging frequency analysis for deep fake image recognition"):
  with members:
    - "everybody s talkin let me talk as you want"
    - "making convolutional networks shift invariant again"
    - "text based editing of talking head video"
    - "learning rich features for image manipulation detection"
    - "attributing fake images to gans learning and analyzing gan fingerprints"
    - "do gans leave artificial fingerprints"
    - "tequilagan how to easily identify gan samples"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "amp authentication of media via provenance"):
  with members:
    - "few shot adversarial learning of realistic neural talking head models"
    - "exposing deep fakes using inconsistent head poses"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "large scale gan training for high fidelity natural image synthesis"
    - "in ictu oculi exposing ai generated fake face videos by detecting eye blinking"
    - "deepfakes a new threat to face recognition assessment and detection"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-6 (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"
    - "detecting gan generated fake images using co occurrence matrices"
    - "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"

Connected through hyperedge HE-7 (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"
    - "detection of deep network generated images using disparities in color components"
    - "progressive growing of gans for improved quality stability and variation"
    - "generative face completion"
    - "recasting residual based local descriptors as convolutional neural networks an application to image forgery detection"

Connected through hyperedge HE-8 (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"
    - "detecting gan generated fake images using co occurrence matrices"
    - "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"

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)