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 "movienet a movie multilayer network model using visual and textual semantic cues"):
  Abstract: Discovering content and stories in movies is one of the most important concepts in multimedia content research studies. Network models have proven to be an efficient choice for this purpose. When an audience watches a movie, they usually compare the characters and the relationships between them. For this reason, most of the models developed so far are based on social networks analysis. They focus essentially on the characters at play. By analyzing characters' interactions, we can obtain a broad picture of the narration's content. Other works have proposed to exploit semantic elements such as scenes, dialogues, etc. However, they are always captured from a single facet. Motivated by these limitations, we introduce in this work a multilayer network model to capture the narration of a movie based on its script, its subtitles, and the movie content. After introducing the model and the extraction process from the raw data, we perform a comparative analysis of the whole 6-movie cycle of the Star Wars saga. Results demonstrate the effectiveness of the proposed framework for video content representation and analysis.

Includes members:
  Member "semantic flow in language networks":
    also connected through other hyperedges:
      (none)
  Member "multilayer network model of movie script":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "extraction and analysis of fictional character networks a survey")
  Member "centrality in modular networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online reactions to the 2017 unite the right rally in charlottesville measuring polarization in twitter networks using media followership")
  Member "movie rating prediction using content based and link stream features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stream graphs and link streams for the modeling of interactions over time")
  Member "vggface2 a dataset for recognising faces across pose and age":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "stream graphs and link streams for the modeling of interactions over time":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finding temporal patterns using algebraic fingerprints")
  Member "face detection with the faster r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning for face recognition a critical analysis")
  Member "centrality measures for networks with community structure":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "introducing multilayer stream graphs and layer centralities")

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.LG(Machine Learning)