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: "learning to detect fake face images in the wild"
  Abstract: Although Generative Adversarial Network (GAN) can be used to generate the realistic image, improper use of these technologies brings hidden concerns. For example, GAN can be used to generate a tampered video for specific people and inappropriate events, creating images that are detrimental to a particular person, and may even affect that personal safety. In this paper, we will develop a deep forgery discriminator (DeepFD) to efficiently and effectively detect the computer-generated images. Directly learning a binary classifier is relatively tricky since it is hard to find the common discriminative features for judging the fake images generated from different GANs. To address this shortcoming, we adopt contrastive loss in seeking the typical features of the synthesized images generated by different GANs and follow by concatenating a classifier to detect such computer-generated images. Experimental results demonstrate that the proposed DeepFD successfully detected 94.7% fake images generated by several state-of-the-art GANs.

Connected through hyperedge HE-1 (induced by source paper "watch your up convolution cnn based generative deep neural networks are failing to reproduce spectral distributions"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "stabilizing gans with octave convolutions"
    - "figr few shot image generation with reptile"
    - "semi few shot attribute translation"
    - "band limited training and inference for convolutional neural networks"
    - "unmasking deepfakes with simple features"
    - "a fourier perspective on model robustness in computer vision"
    - "attributing fake images to gans learning and analyzing gan fingerprints"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "scalable fine grained generated image classification based on deep metric learning"):
  with members:
    - "learning rich features for image manipulation detection"
    - "universal image manipulation detection using deep siamese convolutional neural network"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "do gans leave artificial fingerprints"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "noiseprint a cnn based camera model fingerprint"
    - "a style based generator architecture for generative adversarial networks"
    - "detection of deep network generated images using disparities in color components"

Connected through hyperedge HE-5 (induced by source paper "unmasking deepfakes with simple features"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "attributing fake images to gans learning and analyzing gan fingerprints"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "mesonet a compact facial video forgery detection network"
    - "exposing deep fakes using inconsistent head poses"
    - "detecting gan generated imagery using color cues"
    - "noiseprint a cnn based camera model fingerprint"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-6 (induced by source paper "source generator attribution via inversion"):
  with members:
    - "detecting overfitting of deep generative networks via latent recovery"
    - "do gans leave artificial fingerprints"
    - "attributing fake images to gans analyzing fingerprints in generated images"
    - "spectral normalization for generative adversarial networks"
    - "learning inverse mapping by autoencoder based generative adversarial nets"
    - "progressive growing of gans for improved quality stability and variation"
    - "precise recovery of latent vectors from generative adversarial networks"
    - "generative visual manipulation on the natural image manifold"

Connected through hyperedge HE-7 (induced by source paper "why we need an ai resilient society"):
  with members:
    - "on the measure of intelligence"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "turing s red flag"

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.MM(Multimedia)