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: "unimorph 2 0 universal morphology"
  Abstract: The Universal Morphology UniMorph project is a collaborative effort to improve how NLP handles complex morphology across the world's languages. The project releases annotated morphological data using a universal tagset, the UniMorph schema. Each inflected form is associated with a lemma, which typically carries its underlying lexical meaning, and a bundle of morphological features from our schema. Additional supporting data and tools are also released on a per-language basis when available. UniMorph is based at the Center for Language and Speech Processing (CLSP) at Johns Hopkins University in Baltimore, Maryland and is sponsored by the DARPA LORELEI program. This paper details advances made to the collection, annotation, and dissemination of project resources since the initial UniMorph release described at LREC 2016. lexical resources} }

Connected through hyperedge HE-1 (induced by source paper "linspector multilingual probing tasks for word representations"):
  with members:
    - "linspector web a multilingual probing suite for word representations"
    - "a survey of cross lingual word embedding models"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "xnli evaluating cross lingual sentence representations"
    - "on the complexity and typology of inflectional morphological systems"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "few shot and zero shot learning for historical text normalization"):
  with members:
    - "a large scale comparison of historical text normalization systems"
    - "tied multitask learning for neural speech translation"
    - "xnmt the extensible neural machine translation toolkit"
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "evaluating historical text normalization systems how well do they generalize"
    - "scheduled multi task learning from syntax to translation"
    - "one shot neural cross lingual transfer for paradigm completion"
    - "identifying beneficial task relations for multi task learning in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "lost in evaluation misleading benchmarks for bilingual dictionary induction"):
  with members:
    - "don t forget the long tail a comprehensive analysis of morphological generalization in bilingual lexicon induction"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "an iterative closest point method for unsupervised word translation"
    - "learning multilingual word embeddings in latent metric space a geometric approach"
    - "generalizing procrustes analysis for better bilingual dictionary induction"
    - "non adversarial unsupervised word translation"
    - "unsupervised alignment of embeddings with wasserstein procrustes"

Connected through hyperedge HE-4 (induced by source paper "cross lingual transfer of semantic roles from raw text to semantic roles"):
  with members:
    - "polyglot semantic role labeling"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "transferring semantic roles using translation and syntactic information"
    - "neural semantic role labeling with dependency path embeddings"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"
    - "a universal part of speech tagset"
    - "cross lingual annotation projection of semantic roles"

Connected through hyperedge HE-5 (induced by source paper "what kind of language is hard to language model"):
  with members:
    - "deep contextualized word representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "an analysis of neural language modeling at multiple scales"
    - "spell once summon anywhere a two level open vocabulary language model"
    - "are all languages equally hard to language model"
    - "unsupervised identification of translationese"
    - "neural machine translation of rare words with subword units"
    - "a parallel corpus of translationese"

Connected through hyperedge HE-6 (induced by source paper "morphological irregularity correlates with frequency"):
  with members:
    - "exact hard monotonic attention for character level transduction"
    - "on the complexity and typology of inflectional morphological systems"
    - "unsupervised disambiguation of syncretism in inflected lexicons"

Connected through hyperedge HE-7 (induced by source paper "morphological tagging and lemmatization of albanian a manually annotated corpus and neural models"):
  with members:
    - "universal lemmatizer a sequence to sequence model for lemmatizing universal dependencies treebanks"
    - "morphological study of albanian words and processing with nooj"

Connected through hyperedge HE-8 (induced by source paper "neural transductive learning and beyond morphological generation in the minimal resource setting"):
  with members:
    - "one shot neural cross lingual transfer for paradigm completion"
    - "align and copy uzh at sigmorphon 2017 shared task for morphological reinflection"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "unlabeled data for morphological generation with character based sequence to sequence models"
    - "modeling target side inflection in neural machine translation"
    - "neural multi source morphological reinflection"
    - "neural language correction with character based attention"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"

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)