human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "tag clouds for displaying semantics the case of filmscripts"
  Abstract: We relate tag clouds to other forms of visualization, including planar or reduced dimensionality mapping, and to Kohonen self-organizing maps. Using a modified tag cloud visualization, we incorporate other information into it, including text sequence and most pertinent words. Our notion of word pertinence goes beyond just word frequency and instead takes a word in a mathematical sense as located at the average of all of its pairwise relationships. We capture semantics through context, taken as all pairwise relationships. Our domain of application is that of filmscript analysis. The analysis of filmscripts, always important for cinema, is experiencing a major gain in importance in the context of television. Our objective in this paper is to visualize the semantics of filmscript, and beyond filmscript any other partially structured, time-ordered sequence of text segments. In particular, we develop an innovative approach to plot characterization.

Connected through hyperedge HE-1 (induced by source paper "computational properties of fiction writing and collaborative work"):
  with members:
    - "new methods of analysis of narrative and semantics in support of interactivity"
    - "the structure of narrative the case of film scripts"

Connected through hyperedge HE-2 (induced by source paper "new methods of analysis of narrative and semantics in support of interactivity"):
  with members:
    - "segmentation and nodal points in narrative study of multiple variations of a ballad"
    - "the structure of narrative the case of film scripts"

Connected through hyperedge HE-3 (induced by source paper "segmentation and nodal points in narrative study of multiple variations of a ballad"):
  with members:
    - "the structure of narrative the case of film scripts"
    - "advances in domain independent linear text segmentation"

Question: Please tell me which class the center node 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.AI(Artificial Intelligence)