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: "on the generalization of gan image forensics"
  Abstract: Recently the GAN generated face images are more and more realistic with high-quality, even hard for human eyes to detect. On the other hand, the forensics community keeps on developing methods to detect these generated fake images and try to guarantee the credibility of visual contents. Although researchers have developed some methods to detect generated images, few of them explore the important problem of generalization ability of forensics model. As new types of GANs are emerging fast, the generalization ability of forensics models to detect new types of GAN images is absolutely an essential research topic. In this paper, we explore this problem and propose to use preprocessed images to train a forensic CNN model. By applying similar image level preprocessing to both real and fake training images, the forensics model is forced to learn more intrinsic features to classify the generated and real face images. Our experimental results also prove the effectiveness of the proposed method.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "deep learning for deepfakes creation and detection"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "few shot adversarial learning of realistic neural talking head models"
    - "limits of deepfake detection a robust estimation viewpoint"
    - "exposing deep fakes using inconsistent head poses"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "exposing deepfake videos by detecting face warping artifacts"

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"
    - "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"
    - "swapped face detection using deep learning and subjective assessment"

Connected through hyperedge HE-4 (induced by source paper "towards generalizable forgery detection with locality aware autoencoder"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning credible deep neural networks with rationale regularization"
    - "faceforensics learning to detect manipulated facial images"
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "fighting fake news image splice detection via learned self consistency"
    - "mesonet a compact facial video forgery detection network"
    - "techniques for interpretable machine learning"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"

Connected through hyperedge HE-5 (induced by source paper "incremental learning for the detection and classification of gan generated images"):
  with members:
    - "source generator attribution via inversion"
    - "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"
    - "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"

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)