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: "text mixing shapes the anatomy of rank frequency distributions"
  Abstract: Natural languages are full of rules and exceptions. One of the most famous quantitative rules is Zipf's law, which states that the frequency of occurrence of a word is approximately inversely proportional to its rank. Though this ``law'' of ranks has been found to hold across disparate texts and forms of data, analyses of increasingly large corpora since the late 1990s have revealed the existence of two scaling regimes. These regimes have thus far been explained by a hypothesis suggesting a separability of languages into core and noncore lexica. Here we present and defend an alternative hypothesis that the two scaling regimes result from the act of aggregating texts. We observe that text mixing leads to an effective decay of word introduction, which we show provides accurate predictions of the location and severity of breaks in scaling. Upon examining large corpora from 10 languages in the Project Gutenberg eBooks collection, we find emphatic empirical support for the universality of our claim.

Connected through hyperedge HE-1 (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"
    - "quantifying structure differences in literature using symbolic diversity and entropy criteria"
    - "the emotional arcs of stories are dominated by six basic shapes"
    - "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-2 (induced by source paper "robustness of sentence length measures in written texts"):
  with members:
    - "in narrative texts punctuation marks obey the same statistics as words"
    - "compression and the origins of zipf s law for word frequencies"
    - "modeling the average shortest path length in growth of word adjacency networks"
    - "punctuation effects in english and esperanto texts"
    - "beyond word frequency bursts lulls and scaling in the temporal distributions of words"

Connected through hyperedge HE-3 (induced by source paper "zipf s law holds for phrases not words"):
  with members:
    - "measuring the happiness of large scale written expression songs blogs and presidents"

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)