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 "extraction and analysis of dynamic conversational networks from tv series"):
  Abstract: Identifying and characterizing the dynamics of modern TV series subplots is an open problem. One way is to study the underlying social network of interactions between the characters. Standard dynamic network extraction methods rely on temporal integration, either over the whole considered period, or as a sequence of several time-slices. However, they turn out to be inappropriate in the case of TV series, because the scenes shown on-screen alternatively focus on parallel story lines, and do not necessarily respect a traditional chronology. In this article, we introduce Narrative Smoothing, a novel network extraction method taking advantage of the plot properties to solve some of their limitations. We apply our method to a corpus of three popular series, and compare it to both standard approaches. Narrative smoothing leads to more relevant observations when it comes to the characterization of the protagonists and their relationships, confirming its appropriateness to model the intertwined story lines constituting the plots.

Includes members:
  Member "remembering winter was coming character oriented video summaries of tv series":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "serial speakers a dataset of tv series")
  Member "remembering winter was coming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "extraction and analysis of fictional character networks a survey")
  Member "narrative smoothing dynamic conversational network for the analysis of tv series plots":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "remembering winter was coming character oriented video summaries of tv series")
  Member "audiovisual speaker diarization of tv series":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "serial speakers a dataset of tv series")
  Member "constrained speaker diarization of tv series based on visual patterns":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "serial speakers a dataset of tv series")

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.MM(Multimedia)