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: "resolution of indirect anaphora in japanese sentences using examples x no y y of x"
  Abstract: A noun phrase can indirectly refer to an entity that has already been mentioned. For example, ``I went into an old house last night. The roof was leaking badly and ...'' indicates that ``the roof'' is associated with `` an old house}'', which was mentioned in the previous sentence. This kind of reference (indirect anaphora) has not been studied well in natural language processing, but is important for coherence resolution, language understanding, and machine translation. In order to analyze indirect anaphora, we need a case frame dictionary for nouns that contains knowledge of the relationships between two nouns but no such dictionary presently exists. Therefore, we are forced to use examples of ``X no Y'' (Y of X) and a verb case frame dictionary instead. We tried estimating indirect anaphora using this information and obtained a recall rate of 63% and a precision rate of 68% on test sentences. This indicates that the information of ``X no Y'' is useful to a certain extent when we cannot make use of a noun case frame dictionary. We estimated the results that would be given by a noun case frame dictionary, and obtained recall and precision rates of 71% and 82% respectively. Finally, we proposed a way to construct a noun case frame dictionary by using examples of ``X no Y.''

Connected through hyperedge HE-1 (induced by source paper "metonymy interpretation using x no y examples"):
  with members:
    - "pronoun resolution in japanese sentences using surface expressions and examples"
    - "resolution of verb ellipsis in japanese sentence using surface expressions and examples"

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)