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 priors for probabilistic neural word embeddings"
  Abstract: Word embeddings allow natural language processing systems to share statistical information across related words. These embeddings are typically based on distributional statistics, making it difficult for them to generalize to rare or unseen words. We propose to improve word embeddings by incorporating morphological information, capturing shared sub-word features. Unlike previous work that constructs word embeddings directly from morphemes, we combine morphological and distributional information in a unified probabilistic framework, in which the word embedding is a latent variable. The morphological information provides a prior distribution on the latent word embeddings, which in turn condition a likelihood function over an observed corpus. This approach yields improvements on intrinsic word similarity evaluations, and also in the downstream task of part-of-speech tagging.

Connected through hyperedge HE-1 (induced by source paper "nytwit a dataset of novel words in the new york times"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mimicking word embeddings using subword rnns"
    - "recent trends in deep learning based natural language processing"

Connected through hyperedge HE-2 (induced by source paper "subword elmo"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "a hybrid morpheme word representation for machine translation of morphologically rich languages"
    - "multi task deep neural networks for natural language understanding"
    - "deep enhanced representation for implicit discourse relation recognition"
    - "deep contextualized word representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-3 (induced by source paper "joint entity extraction and assertion detection for clinical text"):
  with members:
    - "relation extraction using explicit context conditioning"
    - "comprehend medical a named entity recognition and relationship extraction web service"
    - "dynamic transfer learning for named entity recognition"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "negbio a high performance tool for negation and uncertainty detection in radiology reports"
    - "enriching word vectors with subword information"
    - "multi task cross lingual sequence tagging from scratch"
    - "multi task domain adaptation for sequence tagging"

Connected through hyperedge HE-4 (induced by source paper "dynamic transfer learning for named entity recognition"):
  with members:
    - "joint entity extraction and assertion detection for clinical text"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "the natural language decathlon multitask learning as question answering"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "label aware double transfer learning for cross specialty medical named entity recognition"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "dynamic multi level multi task learning for sentence simplification"

Connected through hyperedge HE-5 (induced by source paper "incorporating subword information into matrix factorization word embeddings"):
  with members:
    - "character word lstm language models"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "a joint model for word embedding and word morphology"
    - "enriching word vectors with subword information"
    - "charagram embedding words and sentences via character n grams"
    - "enhancing the lexvec distributed word representation model using positional contexts and external memory"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-6 (induced by source paper "spell once summon anywhere a two level open vocabulary language model"):
  with members:
    - "deep contextualized word representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "mimicking word embeddings using subword rnns"
    - "regularizing and optimizing lstm language models"
    - "learning to create and reuse words in open vocabulary neural language modeling"
    - "character level language modeling with hierarchical recurrent neural networks"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "pointer sentinel mixture models"

Connected through hyperedge HE-7 (induced by source paper "morph fitting fine tuning word vector spaces with simple language specific rules"):
  with members:
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "a network based end to end trainable task oriented dialogue system"
    - "a joint model for word embedding and word morphology"
    - "on line active reward learning for policy optimisation in spoken dialogue systems"
    - "continuously learning neural dialogue management"
    - "charagram embedding words and sentences via character n grams"
    - "neural belief tracker data driven dialogue state tracking"
    - "dialog state tracking a machine reading approach using memory network"

Connected through hyperedge HE-8 (induced by source paper "soc2seq social embedding meets conversation model"):
  with members:
    - "a persona based neural conversation model"
    - "topic aware neural response generation"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "node2vec scalable feature learning for networks"
    - "smart reply automated response suggestion for email"
    - "topic augmented neural response generation with a joint attention mechanism"
    - "deltableu a discriminative metric for generation tasks with intrinsically diverse targets"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"

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)