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 morphological tagging for low resource languages"
  Abstract: Morphologically rich languages often lack the annotated linguistic resources required to develop accurate natural language processing tools. We propose models suitable for training morphological taggers with rich tagsets for low-resource languages without using direct supervision. Our approach extends existing approaches of projecting part-of-speech tags across languages, using bitext to infer constraints on the possible tags for a given word type or token. We propose a tagging model using Wsabie, a discriminative embedding-based model with rank-based learning. In our evaluation on 11 languages, on average this model performs on par with a baseline weakly-supervised HMM, while being more scalable. Multilingual experiments show that the method performs best when projecting between related language pairs. Despite the inherently lossy projection, we show that the morphological tags predicted by our models improve the downstream performance of a parser by +0.6 LAS on average.

Connected through hyperedge HE-1 (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"
    - "marrying universal dependencies and universal morphology"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "crosslingual document embedding as reduced rank ridge regression"):
  with members:
    - "unsupervised cross lingual information retrieval using monolingual data only"
    - "structuring wikipedia articles with section recommendations"
    - "multiwiki interlingual text passage alignment in wikipedia"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "cross language question re ranking"
    - "word translation without parallel data"
    - "massively multilingual word embeddings"
    - "improving zero shot learning by mitigating the hubness problem"

Connected through hyperedge HE-3 (induced by source paper "near or far wide range zero shot cross lingual dependency parsing"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "self attention with relative position representations"
    - "unsupervised cross lingual information retrieval using monolingual data only"
    - "deep contextualized word representations"
    - "a comparison of transformer and recurrent neural networks on multilingual neural machine translation"
    - "constituency parsing with a self attentive encoder"
    - "efficient dependency guided named entity recognition"
    - "neural cross lingual coreference resolution and its application to entity linking"

Connected through hyperedge HE-4 (induced by source paper "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "constituency parsing with a self attentive encoder"
    - "efficient dependency guided named entity recognition"
    - "neural cross lingual coreference resolution and its application to entity linking"
    - "one shot neural cross lingual transfer for paradigm completion"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "fast and scalable expansion of natural language understanding functionality for intelligent agents"):
  with members:
    - "enriching word vectors with subword information"
    - "how transferable are neural networks in nlp applications"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "neural factor graph models for cross lingual morphological tagging"):
  with members:
    - "morphological segmentation inside out"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "word representation models for morphologically rich languages in neural machine translation"
    - "neural morphological tagging from characters for morphologically rich languages"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "bidirectional lstm crf models for sequence tagging"
    - "loopy belief propagation for approximate inference an empirical study"

Connected through hyperedge HE-7 (induced by source paper "one shot neural cross lingual transfer for paradigm completion"):
  with members:
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "single model encoder decoder with explicit morphological representation for reinflection"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "multi source neural translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "morphological inflection generation using character sequence to sequence learning"
    - "multi task sequence to sequence learning"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-8 (induced by source paper "cross lingual character level neural morphological tagging"):
  with members:
    - "one shot neural cross lingual transfer for paradigm completion"
    - "on the state of the art of evaluation in neural language models"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "neural morphological tagging from characters for morphologically rich languages"
    - "multi source neural translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "character level convolutional networks for text classification"

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)