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: "a joint model for word embedding and word morphology"
  Abstract: This paper presents a joint model for performing unsupervised morphological analysis on words, and learning a character-level composition function from morphemes to word embeddings. Our model splits individual words into segments, and weights each segment according to its ability to predict context words. Our morphological analysis is comparable to dedicated morphological analyzers at the task of morpheme boundary recovery, and also performs better than word-based embedding models at the task of syntactic analogy answering. Finally, we show that incorporating morphology explicitly into character-level models help them produce embeddings for unseen words which correlate better with human judgments.

Connected through hyperedge HE-1 (induced by source paper "authorship attribution in bangla literature using character level cnn"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "charagram embedding words and sentences via character n grams"
    - "character level and multi channel convolutional neural networks for large scale authorship attribution"
    - "exploring the limits of language modeling"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "character level convolutional networks for text classification"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-2 (induced by source paper "stem driven language models for morphologically rich languages"):
  with members:
    - "morphological segmentation inside out"
    - "compositional representation of morphologically rich input for neural machine translation"
    - "from characters to words to in between do we capture morphology"
    - "character word lstm language models"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "word representation models for morphologically rich languages in neural machine translation"
    - "generalizing and hybridizing count based and neural language models"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-3 (induced by source paper "vcwe visual character enhanced word embeddings"):
  with members:
    - "learning chinese word representations from glyphs of characters"
    - "learning character level compositionality with visual features"
    - "a structured self attentive sentence embedding"
    - "enriching word vectors with subword information"
    - "charagram embedding words and sentences via character n grams"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "effective subword segmentation for text comprehension"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "dual co matching network for multi choice reading comprehension"
    - "i know what you want semantic learning for text comprehension"
    - "graph based bilingual word embedding for statistical machine translation"
    - "open vocabulary learning for neural chinese pinyin ime"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "subword augmented embedding for cloze reading comprehension"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "deep contextualized word representations"
    - "one shot learning for question answering in gaokao history challenge"
    - "sjtu nlp at semeval 2018 task 9 neural hypernym discovery with term embeddings"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "character word lstm language models"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "fast and accurate neural word segmentation for chinese"

Connected through hyperedge HE-6 (induced by source paper "semi supervised multi task word embeddings"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "think globally embed locally locally linear meta embedding of words"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "enriching word vectors with subword information"
    - "recurrent neural network for text classification with multi task learning"

Connected through hyperedge HE-7 (induced by source paper "meta embedding as auxiliary task regularization"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "think globally embed locally locally linear meta embedding of words"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "enriching word vectors with subword information"
    - "recurrent neural network for text classification with multi task learning"
    - "learning word meta embeddings by using ensembles of embedding sets"

Connected through hyperedge HE-8 (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"
    - "morphological priors for probabilistic neural word embeddings"
    - "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"

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)