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 "emotions don t lie a deepfake detection method using audio visual affective cues"):
  Abstract: We present a learning-based multimodal method for detecting real and deepfake videos. To maximize information for learning, we extract and analyze the similarity between the two audio and visual modalities from within the same video. Additionally, we extract and compare affective cues corresponding to emotion from the two modalities within a video to infer whether the input video is "real" or "fake". We propose a deep learning network, inspired by the Siamese network architecture and the triplet loss. To validate our model, we report the AUC metric on two large-scale, audio-visual deepfake detection datasets, DeepFake-TIMIT Dataset and DFDC. We compare our approach with several SOTA deepfake detection methods and report per-video AUC of 84.4% on the DFDC and 96.6% on the DF-TIMIT datasets, respectively.

Includes members:
  Member "multi task learning for detecting and segmenting manipulated facial images and videos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "recurrent convolutional strategies for face manipulation detection in 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 "the deepfake detection challenge dfdc preview dataset":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "celeb df a new dataset for deepfake forensics":
    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 "exposing deep fakes using inconsistent head poses":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "exposing deepfake videos by detecting face warping artifacts":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "capsule forensics using capsule networks to detect forged images and videos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "celeb df a large scale challenging dataset for deepfake forensics")

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)