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: "automatic ranking of mt outputs using approximations"
  Abstract: Since long, research on machine translation has been ongoing. Still, we do not get good translations from MT engines so developed. Manual ranking of these outputs tends to be very time consuming and expensive. Identifying which one is better or worse than the others is a very taxing task. In this paper, we show an approach which can provide automatic ranks to MT outputs (translations) taken from different MT Engines and which is based on N-gram approximations. We provide a solution where no human intervention is required for ranking systems. Further we also show the evaluations of our results which show equivalent results as that of human ranking.

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"
    - "analysing quality of english hindi machine translation engine outputs using bayesian classification"
    - "a lightweight stemmer for gujarati"

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"
    - "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"
    - "a lightweight stemmer for gujarati"
    - "design of english hindi translation memory for efficient translation"

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)