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 fictional character networks a survey"):
  Abstract: A character network is a graph extracted from a narrative, in which vertices represent characters and edges correspond to interactions between them. A number of narrative-related problems can be addressed automatically through the analysis of character networks, such as summarization, classification, or role detection. Character networks are particularly relevant when considering works of fictions (e.g. novels, plays, movies, TV series), as their exploitation allows developing information retrieval and recommendation systems. However, works of fiction possess specific properties making these tasks harder. This survey aims at presenting and organizing the scientific literature related to the extraction of character networks from works of fiction, as well as their analysis. We first describe the extraction process in a generic way, and explain how its constituting steps are implemented in practice, depending on the medium of the narrative, the goal of the network analysis, and other factors. We then review the descriptive tools used to characterize character networks, with a focus on the way they are interpreted in this context. We illustrate the relevance of character networks by also providing a review of applications derived from their analysis. Finally, we identify the limitations of the existing approaches, and the most promising perspectives.

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 "conversational networks for automatic online moderation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wac a corpus of wikipedia conversations for online abuse detection")
  Member "remembering winter was coming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "extraction and analysis of dynamic conversational networks from tv series")
  Member "predicting kills in game of thrones using network properties":
    also connected through other hyperedges:
      (none)
  Member "multilayer network model of movie script":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "movienet a movie multilayer network model using visual and textual semantic cues")
  Member "from word to sense embeddings a survey on vector representations of meaning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tedl a text encryption method based on deep learning")
  Member "i will be there for you six friends in a clique":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the one comparing narrative social network extraction techniques")
  Member "the one comparing narrative social network extraction techniques":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "how the avengers assemble ecological modelling of effective cast sizes for movies")

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.SI(Social and Information Networks)