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 pseudo projected expectation regularization for weakly supervised learning"
  Abstract: We consider a multilingual weakly supervised learning scenario where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide the learning in other languages. Past approaches project labels across bitext and use them as features or gold labels for training. We propose a new method that projects model expectations rather than labels, which facilities transfer of model uncertainty across language boundaries. We encode expectations as constraints and train a discriminative CRF model using Generalized Expectation Criteria (Mann and McCallum, 2010). Evaluated on standard Chinese-English and German-English NER datasets, our method demonstrates F1 scores of 64% and 60% when no labeled data is used. Attaining the same accuracy with supervised CRFs requires 12k and 1.5k labeled sentences. Furthermore, when combined with labeled examples, our method yields significant improvements over state-of-the-art supervised methods, achieving best reported numbers to date on Chinese OntoNotes and German CoNLL-03 datasets.

Connected through hyperedge HE-1 (induced by source paper "modeling named entity embedding distribution into hypersphere"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "cross lingual syntactic transfer with limited resources"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "lexicon infused phrase embeddings for named entity resolution"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-2 (induced by source paper "transfer learning between related tasks using expected label proportions"):
  with members:
    - "exploiting document knowledge for aspect level sentiment classification"
    - "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "interactive attention networks for aspect level sentiment classification"
    - "aspect level sentiment classification with deep memory network"
    - "effective lstms for target dependent sentiment classification"
    - "adam a method for stochastic optimization"
    - "bayesian inference with posterior regularization and applications to infinite latent svms"

Connected through hyperedge HE-3 (induced by source paper "open named entity modeling from embedding distribution"):
  with members:
    - "deep contextualized word representations"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"
    - "word translation without parallel data"
    - "improving multilingual named entity recognition with wikipedia entity type mapping"
    - "neural architectures for named entity recognition"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-4 (induced by source paper "modeling language variation and universals a survey on typological linguistics for natural language processing"):
  with members:
    - "morphological word embeddings"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "xnli evaluating cross lingual sentence representations"
    - "a deep generative model of vowel formant typology"
    - "deep contextualized word representations"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "adversarial propagation and zero shot cross lingual transfer of word vector specialization"
    - "past present future a computational investigation of the typology of tense in 1000 languages"

Connected through hyperedge HE-5 (induced by source paper "neural cross lingual named entity recognition with minimal resources"):
  with members:
    - "deep contextualized word representations"
    - "attention is all you need"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised sequence tagging with bidirectional language models"
    - "model transfer for tagging low resource languages using a bilingual dictionary"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-6 (induced by source paper "cross lingual semantic parsing"):
  with members:
    - "end to end neural coreference resolution"
    - "addressing the data sparsity issue in neural amr parsing"
    - "language to logical form with neural attention"
    - "multilingual open relation extraction using cross lingual projection"
    - "neural machine translation by jointly learning to align and translate"
    - "grammar as a foreign language"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

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"
    - "cross lingual morphological tagging for low resource languages"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "transfer learning for sequence tagging with hierarchical recurrent networks"):
  with members:
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "improving named entity recognition for chinese social media with word segmentation representation learning"
    - "multilingual language processing from bytes"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"
    - "lexicon infused phrase embeddings for named entity resolution"
    - "polyglot distributed word representations for multilingual nlp"

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)