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: "morphological word embeddings"
  Abstract: Linguistic similarity is multi-faceted. For instance, two words may be similar with respect to semantics, syntax, or morphology inter alia. Continuous word-embeddings have been shown to capture most of these shades of similarity to some degree. This work considers guiding word-embeddings with morphologically annotated data, a form of semi-supervised learning, encouraging the vectors to encode a word's morphology, i.e., words close in the embedded space share morphological features. We extend the log-bilinear model to this end and show that indeed our learned embeddings achieve this, using German as a case study.

Connected through hyperedge HE-1 (induced by source paper "using interlinear glosses as pivot in low resource multilingual machine translation"):
  with members:
    - "character aware decoder for translation into morphologically rich languages"
    - "massively parallel cross lingual learning in low resource target language translation"
    - "improving character based decoding using target side morphological information for neural machine translation"
    - "opennmt open source toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"
    - "multi way multilingual neural machine translation with a shared attention mechanism"

Connected through hyperedge HE-2 (induced by source paper "on the importance of subword information for morphological tasks in truly low resource languages"):
  with members:
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "a systematic study of leveraging subword information for learning word representations"
    - "modeling language variation and universals a survey on typological linguistics for natural language processing"
    - "generalizing word embeddings using bag of subwords"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "adapting word embeddings to new languages with morphological and phonological subword representations"

Connected through hyperedge HE-3 (induced by source paper "a systematic study of leveraging subword information for learning word representations"):
  with members:
    - "learning semantic representations for novel words leveraging both form and context"
    - "generalizing word embeddings using bag of subwords"
    - "deep contextualized word representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "adapting word embeddings to new languages with morphological and phonological subword representations"
    - "from characters to words to in between do we capture morphology"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "confusion2vec towards enriching vector space word representations with representational ambiguities"):
  with members:
    - "learning from past mistakes improving automatic speech recognition output via noisy clean phrase context modeling"
    - "neural lattice language models"
    - "neural lattice to sequence models for uncertain inputs"
    - "enriching word vectors with subword information"
    - "multi view recurrent neural acoustic word embeddings"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "bag of tricks for efficient text classification"
    - "lattice based recurrent neural network encoders for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "parsimonious morpheme segmentation with an application to enriching word embeddings"):
  with members:
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "enriching word vectors with subword information"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "bag of tricks for efficient text classification"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "bidirectional lstm crf models for sequence tagging"
    - "character aware neural language models"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-6 (induced by source paper "attending form and context to generate specialized out of vocabularywords representations"):
  with members:
    - "learning semantic representations for novel words leveraging both form and context"
    - "generalizing word embeddings using bag of subwords"
    - "deep contextualized word representations"
    - "mimicking word embeddings using subword rnns"
    - "enriching word vectors with subword information"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-7 (induced by source paper "chinese embedding via stroke and glyph information a dual channel view"):
  with members:
    - "glyce glyph vectors for chinese character representations"
    - "learning chinese word representations from glyphs of characters"
    - "a survey on network embedding"
    - "enriching word vectors with subword information"
    - "charagram embedding words and sentences via character n grams"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "modeling language variation and universals a survey on typological linguistics for natural language processing"):
  with members:
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "xnli evaluating cross lingual sentence representations"
    - "a deep generative model of vowel formant typology"
    - "deep contextualized word representations"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "adversarial propagation and zero shot cross lingual transfer of word vector specialization"
    - "past present future a computational investigation of the typology of tense in 1000 languages"
    - "unsupervised 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)