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: "marrying universal dependencies and universal morphology"
  Abstract: The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects each present schemata for annotating the morphosyntactic details of language. Each project also provides corpora of annotated text in many languages - UD at the token level and UniMorph at the type level. As each corpus is built by different annotators, language-specific decisions hinder the goal of universal schemata. With compatibility of tags, each project's annotations could be used to validate the other's. Additionally, the availability of both type- and token-level resources would be a boon to tasks such as parsing and homograph disambiguation. To ease this interoperability, we present a deterministic mapping from Universal Dependencies v2 features into the UniMorph schema. We validate our approach by lookup in the UniMorph corpora and find a macro-average of 64.13% recall. We also note incompatibilities due to paucity of data on either side. Finally, we present a critical evaluation of the foundations, strengths, and weaknesses of the two annotation projects.

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"
    - "unimorph 2 0 universal morphology"

Connected through hyperedge HE-2 (induced by source paper "does bert agree evaluating knowledge of structure dependence through agreement relations"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "open sesame getting inside bert s linguistic knowledge"
    - "what does bert look at an analysis of bert s attention"
    - "analysis methods in neural language processing a survey"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "cmu 01 at the sigmorphon 2019 shared task on crosslinguality and context in morphology"):
  with members:
    - "a simple joint model for improved contextual neural lemmatization"
    - "75 languages 1 model parsing universal dependencies universally"
    - "modeling composite labels for neural morphological tagging"
    - "neural factor graph models for cross lingual morphological tagging"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improving named entity recognition by jointly learning to disambiguate morphological tags"
    - "contextual parameter generation for universal neural machine translation"
    - "linguistically informed self attention for semantic role labeling"

Connected through hyperedge HE-4 (induced by source paper "universal lemmatizer a sequence to sequence model for lemmatizing universal dependencies treebanks"):
  with members:
    - "universal dependency parsing from scratch"
    - "su rug at the conll sigmorphon 2017 shared task morphological inflection with attentional sequence to sequence models"
    - "conll sigmorphon 2017 shared task universal morphological reinflection in 52 languages"
    - "opennmt open source toolkit for neural machine translation"
    - "unlabeled data for morphological generation with character based sequence to sequence models"
    - "deep biaffine attention for neural dependency parsing"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-5 (induced by source paper "the sigmorphon 2019 shared task morphological analysis in context and cross lingual transfer for inflection"):
  with members:
    - "exact hard monotonic attention for character level transduction"
    - "freezing subnetworks to analyze domain adaptation in neural machine translation"
    - "neural factor graph models for cross lingual morphological tagging"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rapid adaptation of neural machine translation to new languages"
    - "hard non monotonic attention for character level transduction"
    - "transfer learning for low resource neural machine translation"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "a simple joint model for improved contextual neural lemmatization"):
  with members:
    - "neural factor graph models for cross lingual morphological tagging"
    - "hard non monotonic attention for character level transduction"
    - "show attend and tell neural image caption generation with visual attention"
    - "sequence level training with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "data augmentation for context sensitive neural lemmatization using inflection tables and raw text"):
  with members:
    - "imitation learning for neural morphological string transduction"
    - "unlabeled data for morphological generation with character based sequence to sequence models"
    - "using the output embedding to improve language models"

Connected through hyperedge HE-8 (induced by source paper "the conll sigmorphon 2018 shared task universal morphological reinflection"):
  with members:
    - "unimorph 2 0 universal morphology"
    - "unsupervised disambiguation of syncretism in inflected lexicons"
    - "finding the way from a to a sub character morphological inflection for the sigmorphon 2018 shared task"
    - "copenhagen at conll sigmorphon 2018 multilingual inflection in context with explicit morphosyntactic decoding"
    - "a structured variational autoencoder for contextual morphological inflection"
    - "attention is all you need"
    - "align and copy uzh at sigmorphon 2017 shared task for morphological reinflection"
    - "get to the point summarization with pointer generator networks"

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)