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: "one shot neural cross lingual transfer for paradigm completion"
  Abstract: We present a novel cross-lingual transfer method for paradigm completion, the task of mapping a lemma to its inflected forms, using a neural encoder-decoder model, the state of the art for the monolingual task. We use labeled data from a high-resource language to increase performance on a low-resource language. In experiments on 21 language pairs from four different language families, we obtain up to 58% higher accuracy than without transfer and show that even zero-shot and one-shot learning are possible. We further find that the degree of language relatedness strongly influences the ability to transfer morphological knowledge.

Connected through hyperedge HE-1 (induced by source paper "parameter space factorization for zero shot learning across tasks and languages"):
  with members:
    - "the case for bayesian deep learning"
    - "learning and evaluating general linguistic intelligence"
    - "what do language representations really represent"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "mlqa evaluating cross lingual extractive question answering"
    - "how multilingual is multilingual bert"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "cross lingual language model pretraining"

Connected through hyperedge HE-2 (induced by source paper "towards realistic practices in low resource natural language processing the development set"):
  with members:
    - "imitation learning for neural morphological string transduction"
    - "bootstrapping transliteration with constrained discovery for low resource languages"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "diverse few shot text classification with multiple metrics"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "pushing the limits of low resource morphological inflection"):
  with members:
    - "automatic keyboard layout design for low resource latin script languages"
    - "exact hard monotonic attention for character level transduction"
    - "rapid adaptation of neural machine translation to new languages"
    - "leveraging translations for speech transcription in low resource settings"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"
    - "conll sigmorphon 2017 shared task universal morphological reinflection in 52 languages"
    - "controllable invariance through adversarial feature learning"

Connected through hyperedge HE-4 (induced by source paper "few shot and zero shot learning for historical text normalization"):
  with members:
    - "a large scale comparison of historical text normalization systems"
    - "tied multitask learning for neural speech translation"
    - "xnmt the extensible neural machine translation toolkit"
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "evaluating historical text normalization systems how well do they generalize"
    - "unimorph 2 0 universal morphology"
    - "scheduled multi task learning from syntax to translation"
    - "identifying beneficial task relations for multi task learning in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "acquisition of inflectional morphology in artificial neural networks with prior knowledge"):
  with members:
    - "probing what different nlp tasks teach machines about function word comprehension"
    - "neural transductive learning and beyond morphological generation in the minimal resource setting"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "recurrent neural networks in linguistic theory revisiting pinker and prince 1988 and the past tense debate"
    - "get to the point summarization with pointer generator networks"
    - "towards linguistically generalizable nlp systems a workshop and shared task"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "toward multilingual neural machine translation with universal encoder and decoder"

Connected through hyperedge HE-6 (induced by source paper "char rnn for word stress detection in east slavic languages"):
  with members:
    - "automated word stress detection in russian"
    - "cross lingual character level neural morphological tagging"
    - "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-7 (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-8 (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"

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)