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: "unsupervised cross lingual information retrieval using monolingual data only"
  Abstract: We propose a fully unsupervised framework for ad-hoc cross-lingual information retrieval (CLIR) which requires no bilingual data at all. The framework leverages shared cross-lingual word embedding spaces in which terms, queries, and documents can be represented, irrespective of their actual language. The shared embedding spaces are induced solely on the basis of monolingual corpora in two languages through an iterative process based on adversarial neural networks. Our experiments on the standard CLEF CLIR collections for three language pairs of varying degrees of language similarity (English-Dutch/Italian/Finnish) demonstrate the usefulness of the proposed fully unsupervised approach. Our CLIR models with unsupervised cross-lingual embeddings outperform baselines that utilize cross-lingual embeddings induced relying on word-level and document-level alignments. We then demonstrate that further improvements can be achieved by unsupervised ensemble CLIR models. We believe that the proposed framework is the first step towards development of effective CLIR models for language pairs and domains where parallel data are scarce or non-existent.

Connected through hyperedge HE-1 (induced by source paper "irony detection in a multilingual context"):
  with members:
    - "a corpus of english hindi code mixed tweets for sarcasm detection"
    - "bilingual sentiment embeddings joint projection of sentiment across languages"
    - "learning word vectors for 157 languages"
    - "unsupervised cross lingual word embedding by multilingual neural language models"
    - "unsupervised neural machine translation"
    - "word translation without parallel data"
    - "improving multilingual named entity recognition with wikipedia entity type mapping"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-2 (induced by source paper "generalized data augmentation for low resource translation"):
  with members:
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "unsupervised neural machine translation with smt as posterior regularization"
    - "choosing transfer languages for cross lingual learning"
    - "massively multilingual neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "style transfer as unsupervised machine translation"
    - "universal neural machine translation for extremely low resource languages"
    - "multi source neural machine translation with data augmentation"

Connected through hyperedge HE-3 (induced by source paper "teaching a new dog old tricks resurrecting multilingual retrieval using zero shot learning"):
  with members:
    - "cross lingual relevance transfer for document retrieval"
    - "simple applications of bert for ad hoc document retrieval"
    - "deeper text understanding for ir with contextual neural language modeling"
    - "cedr contextualized embeddings for document ranking"
    - "cross lingual language model pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "cross lingual transfer learning for multilingual task oriented dialog"
    - "end to end neural ad hoc ranking with kernel pooling"

Connected through hyperedge HE-4 (induced by source paper "bilingual lexicon induction through unsupervised machine translation"):
  with members:
    - "an effective approach to unsupervised machine translation"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "gromov wasserstein alignment of word embedding spaces"
    - "unsupervised statistical machine translation"
    - "non adversarial unsupervised word translation"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "learning unsupervised word mapping by maximizing mean discrepancy"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-5 (induced by source paper "crosslingual document embedding as reduced rank ridge regression"):
  with members:
    - "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"
    - "cross lingual morphological tagging for low resource languages"
    - "improving zero shot learning by mitigating the hubness problem"

Connected through hyperedge HE-6 (induced by source paper "query expansion for cross language question re ranking"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "uh prhlt at semeval 2016 task 3 combining lexical and semantic based features for community question answering"
    - "query expansion with locally trained word embeddings"

Connected through hyperedge HE-7 (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"
    - "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"
    - "neural cross lingual named entity recognition with minimal resources"

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)