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: "quality estimation of english hindi outputs using naive bayes classifier"
  Abstract: In this paper we present an approach for estimating the quality of machine translation system. There are various methods for estimating the quality of output sentences, but in this paper we focus on Na\"ive Bayes classifier to build model using features which are extracted from the input sentences. These features are used for finding the likelihood of each of the sentences of the training data which are then further used for determining the scores of the test data. On the basis of these scores we determine the class labels of the test data.

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"
    - "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"
    - "a lightweight stemmer for gujarati"

Connected through hyperedge HE-2 (induced by source paper "quality estimation of machine translation outputs through stemming"):
  with members:
    - "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"
    - "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)