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: "a rhetorical analysis approach to natural language processing"
  Abstract: The goal of this research was to find a way to extend the capabilities of computers through the processing of language in a more human way, and present applications which demonstrate the power of this method. This research presents a novel approach, Rhetorical Analysis, to solving problems in Natural Language Processing (NLP). The main benefit of Rhetorical Analysis, as opposed to previous approaches, is that it does not require the accumulation of large sets of training data, but can be used to solve a multitude of problems within the field of NLP. The NLP problems investigated with Rhetorical Analysis were the Author Identification problem - predicting the author of a piece of text based on its rhetorical strategies, Election Prediction - predicting the winner of a presidential candidate's re-election campaign based on rhetorical strategies within that president's inaugural address, Natural Language Generation - having a computer produce text containing rhetorical strategies, and Document Summarization. The results of this research indicate that an Author Identification system based on Rhetorical Analysis could predict the correct author 100% of the time, that a re-election predictor based on Rhetorical Analysis could predict the correct winner of a re-election campaign 55% of the time, that a Natural Language Generation system based on Rhetorical Analysis could output text with up to 87.3% similarity to Shakespeare in style, and that a Document Summarization system based on Rhetorical Analysis could extract highly relevant sentences. Overall, this study demonstrated that Rhetorical Analysis could be a useful approach to solving problems in NLP.

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)