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: "cross lingual character level neural morphological tagging"
  Abstract: Even for common NLP tasks, sufficient supervision is not available in many languages -- morphological tagging is no exception. In the work presented here, we explore a transfer learning scheme, whereby we train character-level recurrent neural taggers to predict morphological taggings for high-resource languages and low-resource languages together. Learning joint character representations among multiple related languages successfully enables knowledge transfer from the high-resource languages to the low-resource ones, improving accuracy by up to 30%

Connected through hyperedge HE-1 (induced by source paper "char rnn for word stress detection in east slavic languages"):
  with members:
    - "automated word stress detection in russian"
    - "one shot neural cross lingual transfer for paradigm completion"
    - "syntaxnet models for the conll 2017 shared task"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "massively multilingual word embeddings"
    - "bag of tricks for efficient text classification"
    - "multilingual distributed representations without word alignment"

Connected through hyperedge HE-2 (induced by source paper "morphological analysis using a sequence decoder"):
  with members:
    - "neural factor graph models for cross lingual morphological tagging"
    - "a morphology aware network for morphological disambiguation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "exploiting cross lingual subword similarities in low resource document classification"):
  with members:
    - "don t forget the long tail a comprehensive analysis of morphological generalization in bilingual lexicon induction"
    - "a resource free evaluation metric for cross lingual word embeddings based on graph modularity"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "interactive refinement of cross lingual word embeddings"
    - "gromov wasserstein alignment of word embedding spaces"
    - "zero shot neural transfer for cross lingual entity linking"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "multi task deep morphological analyzer context aware joint morphological tagging and lemma prediction"):
  with members:
    - "neural machine translation for bilingually scarce scenarios a deep multi task learning approach"
    - "meta multi task learning for sequence modeling"
    - "scheduled multi task learning from syntax to translation"
    - "online and linear time attention by enforcing monotonic alignments"
    - "improving generalization performance by switching from adam to sgd"
    - "recent trends in deep learning based natural language processing"
    - "exploiting linguistic resources for neural machine translation using multi task learning"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-5 (induced by source paper "zero shot neural transfer for cross lingual entity linking"):
  with members:
    - "model transfer for tagging low resource languages using a bilingual dictionary"
    - "neural cross lingual entity linking"
    - "neural models for information retrieval"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "neural architectures for named entity recognition"
    - "transfer learning for low resource neural machine translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"

Connected through hyperedge HE-6 (induced by source paper "cross lingual argumentation mining machine translation and a bit of projection is all you need"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "multi task learning for argumentation mining in low resource settings"
    - "neural end to end learning for computational argumentation mining"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "crowdsourcing argumentation structures in chinese hotel reviews"
    - "cross lingual and cross domain discourse segmentation of entire documents"
    - "cross lingual models of word embeddings an empirical comparison"
    - "end to end sequence labeling via bi directional lstm cnns crf"

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)