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: "analysing quality of english hindi machine translation engine outputs using bayesian classification"
  Abstract: This paper considers the problem for estimating the quality of machine translation outputs which are independent of human intervention and are generally addressed using machine learning techniques.There are various measures through which a machine learns translations quality. Automatic Evaluation metrics produce good co-relation at corpus level but cannot produce the same results at the same segment or sentence level. In this paper 16 features are extracted from the input sentences and their translations and a quality score is obtained based on Bayesian inference produced from training data.

Connected through hyperedge HE-1 (induced by source paper "towards an arabic english machine translation based on semantic web"):
  with members:
    - "using linguistic analysis to translate arabic natural language queries to sparql"
    - "using ontology based context in the portuguese english translation of homographs in textual dialogues"

Connected through hyperedge HE-2 (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"
    - "a lightweight stemmer for gujarati"

Connected through hyperedge HE-3 (induced by source paper "using ontology based context in the portuguese english translation of homographs in textual dialogues"):
  with members:
    - "using a bag of words for automatic medical image annotation with a latent semantic"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "automatic ranking of mt outputs using approximations"):
  with members:
    - "heval yet another human evaluation metric"
    - "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)