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: "attributing fake images to gans analyzing fingerprints in generated images"
  Abstract: Research in computer graphics has been in pursuit of realistic image generation for a long time. Recent advances in machine learning with deep generative models have shown increasing success of closing the realism gap by using data-driven and learned components. There is an increasing concern that real and fake images will become more and more difficult to tell apart. We take a first step towards this larger research challenge by asking the question if and to what extend a generated fake image can be attribute to a particular Generative Adversarial Networks (GANs) of a certain architecture and trained with particular data and random seed. Our analysis shows single samples from GANs carry highly characteristic fingerprints which make attribution of images to GANs possible. Surprisingly, this is even possible for GANs with same architecture and same training that only differ by the training seed.

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 "deep neural network fingerprinting by conferrable adversarial examples"):
  with members:
    - "structure matters towards generating transferable adversarial images"
    - "high fidelity extraction of neural network models"
    - "on the robustness of the backdoor based watermarking in deep neural networks"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "ipguard protecting the intellectual property of deep neural networks via fingerprinting the classification boundary"
    - "how to prove your model belongs to you a blind watermark based framework to protect intellectual property of dnn"
    - "dawn dynamic adversarial watermarking of neural networks"
    - "on evaluating adversarial robustness"

Connected through hyperedge HE-3 (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-4 (induced by source paper "cnn generated images are surprisingly easy to spot for now"):
  with members:
    - "seeing what a gan cannot generate"
    - "detecting gan generated fake images using co occurrence matrices"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "analyzing and improving the image quality of stylegan"):
  with members:
    - "style generator inversion for image enhancement and animation"
    - "source generator attribution via inversion"
    - "image2stylegan how to embed images into the stylegan latent space"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "msg gan multi scale gradient gan for stable image synthesis"
    - "interpreting the latent space of gans for semantic face editing"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "spectral normalization for generative adversarial networks"

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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "incremental learning for the detection and classification of gan generated images"):
  with members:
    - "source generator attribution via inversion"
    - "on the generalization of gan image forensics"
    - "exposing gan synthesized faces using landmark locations"
    - "do gans leave artificial fingerprints"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "revisiting distillation and incremental classifier learning"
    - "detecting gan generated imagery using color cues"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-8 (induced by source paper "detecting and simulating artifacts in gan fake images"):
  with members:
    - "detecting gan generated fake images using co occurrence matrices"
    - "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"
    - "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)