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 "forensictransfer weakly supervised domain adaptation for forgery detection"):
  Abstract: Distinguishing manipulated from real images is becoming increasingly difficult as new sophisticated image forgery approaches come out by the day. Naive classification approaches based on Convolutional Neural Networks (CNNs) show excellent performance in detecting image manipulations when they are trained on a specific forgery method. However, on examples from unseen manipulation approaches, their performance drops significantly. To address this limitation in transferability, we introduce Forensic-Transfer (FT). We devise a learning-based forensic detector which adapts well to new domains, i.e., novel manipulation methods and can handle scenarios where only a handful of fake examples are available during training. To this end, we learn a forensic embedding based on a novel autoencoder-based architecture that can be used to distinguish between real and fake imagery. The learned embedding acts as a form of anomaly detector; namely, an image manipulated from an unseen method will be detected as fake provided it maps sufficiently far away from the cluster of real images. Comparing to prior works, FT shows significant improvements in transferability, which we demonstrate in a series of experiments on cutting-edge benchmarks. For instance, on unseen examples, we achieve up to 85% in terms of accuracy, and with only a handful of seen examples, our performance already reaches around 95%.

Includes members:
  Member "detecting and simulating artifacts in gan fake images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "a closer look at few shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "revisiting meta learning as supervised learning")
  Member "learning rich features for image manipulation 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 "fighting fake news image splice detection via learned self consistency":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "mesonet a compact facial video forgery detection network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "faceforensics a large scale video dataset for forgery detection in human faces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "noiseprint a cnn based camera model fingerprint":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cnn based fast source device identification")
  Member "image splicing localization using a multi task fully convolutional network mfcn":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object discovery with a copy pasting gan")

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)