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: "reordering rules for english hindi smt"
  Abstract: Reordering is a preprocessing stage for Statistical Machine Translation (SMT) system where the words of the source sentence are reordered as per the syntax of the target language. We are proposing a rich set of rules for better reordering. The idea is to facilitate the training process by better alignments and parallel phrase extraction for a phrase-based SMT system. Reordering also helps the decoding process and hence improving the machine translation quality. We have observed significant improvements in the translation quality by using our approach over the baseline SMT. We have used BLEU, NIST, multi-reference word error rate, multi-reference position independent error rate for judging the improvements. We have exploited open source SMT toolkit MOSES to develop the system.

Connected through hyperedge HE-1 (induced by source paper "utilizing language relatedness to improve machine translation a case study on languages of the indian subcontinent"):
  with members:
    - "orthographic syllable as basic unit for smt between related languages"
    - "learning variable length units for smt between related languages via byte pair encoding"
    - "faster decoding for subword level phrase based smt between related languages"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-2 (induced by source paper "english bhojpuri smt system insights from the karaka model"):
  with members:
    - "the rgnlp machine translation systems for wat 2018"
    - "automatic identification of closely related indian languages resources and experiments"
    - "demo of sanskrit hindi smt system"
    - "joining hands exploiting monolingual treebanks for parsing of code mixing data"
    - "opennmt open source toolkit for neural machine translation"
    - "modeling source syntax for neural machine translation"
    - "nematus a toolkit for neural machine translation"
    - "six challenges for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "statistical machine translation for indian languages mission hindi 2"):
  with members:
    - "empirical methods for compound splitting"

Connected through hyperedge HE-4 (induced by source paper "classifier based text simplification for improved machine translation"):
  with members:
    - "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"
    - "a lightweight stemmer for gujarati"

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)