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 "fighting fake news image splice detection via learned self consistency"):
  Abstract: Advances in photo editing and manipulation tools have made it significantly easier to create fake imagery. Learning to detect such manipulations, however, remains a challenging problem due to the lack of sufficient amounts of manipulated training data. In this paper, we propose a learning algorithm for detecting visual image manipulations that is trained only using a large dataset of real photographs. The algorithm uses the automatically recorded photo EXIF metadata as supervisory signal for training a model to determine whether an image is self-consistent -- that is, whether its content could have been produced by a single imaging pipeline. We apply this self-consistency model to the task of detecting and localizing image splices. The proposed method obtains state-of-the-art performance on several image forensics benchmarks, despite never seeing any manipulated images at training. That said, it is merely a step in the long quest for a truly general purpose visual forensics tool.

Includes members:
  Member "learning rich features for image manipulation detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "image splicing localization using a multi task fully convolutional network mfcn":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object discovery with a copy pasting gan")
  Member "two stream neural networks for tampered face detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "first steps toward camera model identification with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cnn based fast source device identification")
  Member "aligned and non aligned double jpeg detection using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "harmonic convolutional networks based on discrete cosine transform")
  Member "contrast enhancement estimation for digital image forensics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dual domain fusion convolutional neural network for contrast enhancement forensics")
  Member "deep image harmonization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generating object stamps")
  Member "you said that":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "audio driven talking face video generation with learning based personalized head pose")

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)