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 "on the detection of digital face manipulation"):
  Abstract: Detecting manipulated facial images and videos is an increasingly important topic in digital media forensics. As advanced synthetic face generation and manipulation methods become available, new types of fake face representations are being created and raise significant concerns for their implications in social media. Hence, it is crucial to detect the manipulated face image and locate manipulated facial regions. Instead of simply using a multi-task learning approach to simultaneously detect manipulated images and predict the manipulated mask (regions), we propose to utilize the attention mechanism to process and improve the feature maps of the classifier model. The learned attention maps highlight the informative regions to further improve the binary classification power, and also visualize the manipulated regions. In addition, to enable our study of manipulated facial images detection and localization, we have collected the first database which contains numerous types of facial forgeries. With this dataset, we perform a thorough analysis of data-driven fake face detection. We demonstrate that the use of an attention mechanism improves manipulated facial region localization and fake detection.

Includes members:
  Member "attention based dropout layer for weakly supervised object localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dual attention guided dropblock module for weakly supervised object localization")
  Member "multi task learning for detecting and segmenting manipulated facial images and videos":
    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 "advfaces adversarial face synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spoc spoofing camera fingerprints")
  Member "deep tree learning for zero shot face anti spoofing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost")
  Member "representation learning by rotating your faces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")
  Member "on learning 3d face morphable model from in the wild images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "retinaface single stage dense face localisation in the wild")
  Member "fakespotter a simple baseline for spotting ai synthesized fake faces":
    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)