human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "real or fake spoofing state of the art face synthesis detection systems"):
  Abstract: The availability of large-scale facial databases, together with the remarkable progresses of deep learning technologies, in particular Generative Adversarial Networks (GANs), have led to the generation of extremely realistic fake facial content, which raises obvious concerns about the potential for misuse. These concerns have fostered the research of manipulation detection methods that, contrary to humans, have already achieved astonishing results in some scenarios. In this study, we focus on the entire face synthesis, which is one specific type of facial manipulation. The main contributions of this study are: i) a novel strategy to remove GAN "fingerprints" from synthetic fake images in order to spoof facial manipulation detection systems, while keeping the visual quality of the resulting images, ii) an in-depth analysis of state-of-the-art detection approaches for the entire face synthesis manipulation, iii) a complete experimental assessment of this type of facial manipulation considering state-of-the-art detection systems, remarking how challenging is this task in unconstrained scenarios, and finally iv) a novel public database named FSRemovalDB produced after applying our proposed GAN-fingerprint removal approach to original synthetic fake images. #R##N#The observed results led us to conclude that more efforts are required to develop robust facial manipulation detection systems against unseen conditions and spoof techniques such as the one proposed in this study.

Includes members:
  Member "biometric presentation attack detection beyond the visible spectrum":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cnn hyperparameter tuning applied to iris liveness detection")
  Member "detecting gan generated fake images using co occurrence matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "faceforensics learning to detect manipulated facial images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "watch your up convolution cnn based generative deep neural networks are failing to reproduce spectral distributions")
  Member "the deepfake detection challenge dfdc preview dataset":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "fakespotter a simple baseline for spotting ai synthesized fake faces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "detecting and simulating artifacts in gan fake images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "fakespotter a simple yet robust baseline for spotting ai synthesized fake faces":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "on the detection of digital face manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")

Question: Please tell me which class the source paper 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)