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: "punctuation effects in english and esperanto texts"
  Abstract: A statistical physics study of punctuation effects on sentence lengths is presented for written texts: Alice in wonderland and Through a looking glass. The translation of the first text into esperanto is also considered as a test for the role of punctuation in defining a style, and for contrasting natural and artificial, but written, languages. Several log–log plots of the sentence-length–rank relationship are presented for the major punctuation marks. Different power laws are observed with characteristic exponents. The exponent can take a value much less than unity (ca. 0.50 or 0.30) depending on how a sentence is defined. The texts are also mapped into time series based on the word frequencies. The quantitative differences between the original and translated texts are very minutes, at the exponent level. It is argued that sentences seem to be more reliable than word distributions in discussing an author style.

Connected through hyperedge HE-1 (induced by source paper "a joint text mining rank size investigation of the rhetoric structures of the us presidents speeches"):
  with members:
    - "sentiment analysis based on rhetorical structure theory learning deep neural networks from discourse trees"
    - "zipf s law and the creation of musical context"

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"
    - "text mixing shapes the anatomy of rank frequency distributions"
    - "modeling the average shortest path length in growth of word adjacency networks"
    - "beyond word frequency bursts lulls and scaling in the temporal distributions of words"

Connected through hyperedge HE-3 (induced by source paper "in narrative texts punctuation marks obey the same statistics as words"):
  with members:
    - "a complex network approach to stylometry"
    - "modeling the average shortest path length in growth of word adjacency networks"
    - "structure semantics interplay in complex networks and its effects on the predictability of similarity in texts"
    - "linguistic complexity english vs polish text vs corpus"
    - "beyond word frequency bursts lulls and scaling in the temporal distributions of words"

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)