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: "source generator attribution via inversion"
  Abstract: With advances in Generative Adversarial Networks (GANs) leading to dramatically-improved synthetic images and video, there is an increased need for algorithms which extend traditional forensics to this new category of imagery. While GANs have been shown to be helpful in a number of computer vision applications, there are other problematic uses such as `deep fakes' which necessitate such forensics. Source camera attribution algorithms using various cues have addressed this need for imagery captured by a camera, but there are fewer options for synthetic imagery. We address the problem of attributing a synthetic image to a specific generator in a white box setting, by inverting the process of generation. This enables us to simultaneously determine whether the generator produced the image and recover an input which produces a close match to the synthetic image.

Connected through hyperedge HE-1 (induced by source paper "spoc spoofing camera fingerprints"):
  with members:
    - "fsgan subject agnostic face swapping and reenactment"
    - "faceforensics learning to detect manipulated facial images"
    - "advfaces adversarial face synthesis"
    - "at gan a generative attack model for adversarial transferring on generative adversarial nets"
    - "exposing gan synthesized faces using landmark locations"
    - "detecting and simulating artifacts in gan fake images"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "make a face towards arbitrary high fidelity face manipulation"

Connected through hyperedge HE-2 (induced by source paper "analyzing and improving the image quality of stylegan"):
  with members:
    - "style generator inversion for image enhancement and animation"
    - "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"
    - "attributing fake images to gans analyzing fingerprints in generated images"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "incremental learning for the detection and classification of gan generated images"):
  with members:
    - "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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "detecting and simulating artifacts in gan fake images"):
  with members:
    - "detecting gan generated fake images using co occurrence matrices"
    - "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)