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 "generate segment and replace towards generic manipulation segmentation"):
  Abstract: It has been witnessed an emerging demand for image manipulation segmentation to distinguish between fake images produced by advanced photo editing software and authentic ones. In this paper, we describe an approach based on semantic segmentation for detecting image manipulation. The approach consists of three stages. A generation stage generates hard manipulated images from authentic images using a Generative Adversarial Network (GAN) based model by cutting a region out of a training sample, pasting it into an authentic image and then passing the image through a GAN to generate harder true positive tampered region. A segmentation stage and a replacement stage, sharing weights with each other, then collaboratively construct dense predictions of tampered regions. We achieve state-of-the-art performance on four public image manipulation detection benchmarks while maintaining robustness to various attacks.

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 provenance analysis at scale":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beyond pixels image provenance analysis leveraging metadata")
  Member "fighting fake news image splice detection via learned self consistency":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "forensictransfer weakly supervised domain adaptation for forgery detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploiting human social cognition for the detection of fake and fraudulent faces via memory networks")
  Member "image splicing localization using a multi task fully convolutional network mfcn":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "a d net training a shadow detector with adversarial shadow attenuation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "where is my mirror")
  Member "a fast rcnn hard positive generation via adversary for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on leveraging pretrained gans for limited data generation")

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)