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: "quantifying structure differences in literature using symbolic diversity and entropy criteria"
  Abstract: AbstractWe measured entropy and symbolic diversity of texts written in English and Spanish. We included texts by Literature Nobel laureates and other famous authors. We formed four groups of texts according to the combinations of language used and the author’s Literature Nobel Prize condition. Entropy, symbol diversity and symbol frequency profiles were compared for these four groups. We also built a scale sensitive to the quality of writing and evaluated its relationship with the Flesch’s readability index for English and the Szigriszt’s perspicuity index for Spanish. Results suggest a correlation between entropy and word diversity with quality of writing. Text genre also influences the resulting entropy and diversity of the text. Results suggest the plausibility of automated quality assessment of texts.

Connected through hyperedge HE-1 (induced by source paper "basis to develop a platform for multiple scale complex systems modeling and visualization monet"):
  with members:
    - "visual analytics for explainable deep learning"
    - "calculating entropy at different scales among diverse communication systems"
    - "complexity measurement of natural and artificial languages"
    - "music viewed by its entropy content a novel window for comparative analysis"

Connected through hyperedge HE-2 (induced by source paper "a standardized project gutenberg corpus for statistical analysis of natural language and quantitative linguistics"):
  with members:
    - "natural language statistical features of lstm generated texts"
    - "mapping the americanization of english in space and time"
    - "in narrative texts punctuation marks obey the same statistics as words"
    - "the emotional arcs of stories are dominated by six basic shapes"
    - "text mixing shapes the anatomy of rank frequency distributions"
    - "rank diversity of languages generic behavior in computational linguistics"
    - "a practical approach to language complexity a wikipedia case study"
    - "collaborative tagging and semiotic dynamics"

Connected through hyperedge HE-3 (induced by source paper "calculating entropy at different scales among diverse communication systems"):
  with members:
    - "a fundamental scale of descriptions for analyzing information content of communication systems"
    - "complexity measurement of natural and artificial languages"
    - "complexity and information measuring emergence self organization and homeostasis at multiple scales"

Connected through hyperedge HE-4 (induced by source paper "a fundamental scale of descriptions for analyzing information content of communication systems"):
  with members:
    - "complexity measurement of natural and artificial languages"

Connected through hyperedge HE-5 (induced by source paper "the fundamental scale of descriptions"):
  with members:
    - "complexity measurement of natural and artificial languages"

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.CL(Computation and Language)