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: "empirical methods for compound splitting"
  Abstract: Compounded words are a challenge for NLP applications such as machine translation (MT). We introduce methods to learn splitting rules from monolingual and parallel corpora. We evaluate them against a gold standard and measure their impact on performance of statistical MT systems. Results show accuracy of 99.1% and performance gains for MT of 0.039 BLEU on a German-English noun phrase translation task.

Connected through hyperedge HE-1 (induced by source paper "on the linguistic representational power of neural machine translation models"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the emergence of number and syntax units in lstm language models"
    - "a hybrid morpheme word representation for machine translation of morphologically rich languages"
    - "linguistic knowledge and transferability of contextual representations"
    - "the missing ingredient in zero shot neural machine translation"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "bpe and charcnns for translation of morphology a cross lingual comparison and analysis"

Connected through hyperedge HE-2 (induced by source paper "char rnn and active learning for hashtag segmentation"):
  with members:
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "an end to end neural natural language interface for databases"
    - "universal word segmentation implementation and interpretation"
    - "deep active learning for named entity recognition"
    - "a gap based framework for chinese word segmentation via very deep convolutional networks"
    - "attention is all you need"
    - "fast and accurate neural word segmentation for chinese"
    - "neural word segmentation learning for chinese"

Connected through hyperedge HE-3 (induced by source paper "multi task pairwise neural ranking for hashtag segmentation"):
  with members:
    - "microblog hashtag generation via encoding conversation contexts"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "exploring the limits of weakly supervised pretraining"
    - "emo love and god making sense of urban dictionary a crowd sourced online dictionary"
    - "tweet2vec character based distributed representations for social media"
    - "towards deep semantic analysis of hashtags"
    - "adam a method for stochastic optimization"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-4 (induced by source paper "comparing neural and n gram based language models for word segmentation"):
  with members:
    - "a non projective greedy dependency parser with bidirectional lstms"
    - "neural word segmentation learning for chinese"
    - "rnn recurrent library for torch"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "recurrent neural network regularization"

Connected through hyperedge HE-5 (induced by source paper "augmenting statistical machine translation with subword translation of out of vocabulary words"):
  with members:
    - "opennmt open source toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "enriching word vectors with subword information"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-6 (induced by source paper "splitting source code identifiers using bidirectional lstm recurrent neural network"):
  with members:
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-7 (induced by source paper "what do neural machine translation models learn about morphology"):
  with members:
    - "a hybrid morpheme word representation for machine translation of morphologically rich languages"
    - "challenging language dependent segmentation for arabic an application to machine translation and part of speech tagging"
    - "a multifaceted evaluation of neural versus phrase based machine translation for 9 language directions"
    - "character based neural machine translation"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "representation of linguistic form and function in recurrent neural networks"
    - "word representation models for morphologically rich languages in neural machine translation"
    - "achieving open vocabulary neural machine translation with hybrid word character models"

Connected through hyperedge HE-8 (induced by source paper "pre translation for neural machine translation"):
  with members:
    - "log linear combinations of monolingual and bilingual neural machine translation models for automatic post editing"
    - "incorporating discrete translation lexicons into neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "addressing the rare word problem in neural machine 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)