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 lightweight stemmer for gujarati"
  Abstract: Gujarati is a resource poor language with almost no language processing tools being available. In this paper we have shown an implementation of a rule based stemmer of Gujarati. We have shown the creation of rules for stemming and the richness in morphology that Gujarati possesses. We have also evaluated our results by verifying it with a human expert.

Connected through hyperedge HE-1 (induced by source paper "classifier based text simplification for improved machine translation"):
  with members:
    - "reordering rules for english hindi smt"
    - "quality estimation of machine translation outputs through stemming"
    - "quality estimation of english hindi outputs using naive bayes classifier"
    - "improving the quality of gujarati hindi machine translation through part of speech tagging and stemmer assisted transliteration"
    - "automatic ranking of mt outputs using approximations"
    - "analysing quality of english hindi machine translation engine outputs using bayesian classification"

Connected through hyperedge HE-2 (induced by source paper "quality estimation of machine translation outputs through stemming"):
  with members:
    - "quality estimation of english hindi outputs using naive bayes classifier"
    - "development of a hindi lemmatizer"
    - "improving the quality of gujarati hindi machine translation through part of speech tagging and stemmer assisted transliteration"
    - "automatic ranking of mt outputs using approximations"
    - "analysing quality of english hindi machine translation engine outputs using bayesian classification"
    - "subjective and objective evaluation of english to urdu machine translation"
    - "heval yet another human evaluation metric"
    - "design of english hindi translation memory for efficient translation"

Connected through hyperedge HE-3 (induced by source paper "rule based stemmer in urdu"):
  with members:
    - "development of a hindi lemmatizer"
    - "improving the quality of gujarati hindi machine translation through part of speech tagging and stemmer assisted transliteration"

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)