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: "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"
  Abstract: We present an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content of Wikipedia articles in 85 languages, including several dialects or low-resource languages. We do not limit the the extraction process to alignments with English, but systematically consider all possible language pairs. In total, we are able to extract 135M parallel sentences for 1620 different language pairs, out of which only 34M are aligned with English. This corpus of parallel sentences is freely available at this https URL. To get an indication on the quality of the extracted bitexts, we train neural MT baseline systems on the mined data only for 1886 languages pairs, and evaluate them on the TED corpus, achieving strong BLEU scores for many language pairs. The WikiMatrix bitexts seem to be particularly interesting to train MT systems between distant languages without the need to pivot through English.

Connected through hyperedge HE-1 (induced by source paper "pmindia a collection of parallel corpora of languages of india"):
  with members:
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "a call for clarity in reporting bleu scores"
    - "the iit bombay english hindi parallel corpus"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-2 (induced by source paper "emerging cross lingual structure in pretrained language models"):
  with members:
    - "similarity of neural network representations revisited"
    - "cross lingual bert transformation for zero shot dependency parsing"
    - "unicoder a universal language encoder by pre training with multiple cross lingual tasks"
    - "polyglot contextual representations improve crosslingual transfer"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "bilingual lexicon induction with semi supervision in non isometric embedding spaces"
    - "investigating multilingual nmt representations at scale"

Connected through hyperedge HE-3 (induced by source paper "mlqa evaluating cross lingual extractive question answering"):
  with members:
    - "neural arabic question answering"
    - "cross lingual transfer learning for question answering"
    - "beyond english only reading comprehension experiments in zero shot multilingual transfer for bulgarian"
    - "unsupervised question answering by cloze translation"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "xcmrc evaluating cross lingual machine reading comprehension"
    - "korquad1 0 korean qa dataset for machine reading comprehension"
    - "xlda cross lingual data augmentation for natural language inference and question answering"

Connected through hyperedge HE-4 (induced by source paper "mass a large and clean multilingual corpus of sentence aligned spoken utterances extracted from the bible"):
  with members:
    - "europarl st a multilingual corpus for speech translation of parliamentary debates"
    - "attention passing models for robust and data efficient end to end speech translation"
    - "massively multilingual neural machine translation"
    - "direct speech to speech translation with a sequence to sequence model"
    - "massively multilingual adversarial speech recognition"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "towards unsupervised speech to text translation"
    - "end to end automatic speech translation of audiobooks"

Connected through hyperedge HE-5 (induced by source paper "should all cross lingual embeddings speak english"):
  with members:
    - "don t forget the long tail a comprehensive analysis of morphological generalization in bilingual lexicon induction"
    - "density matching for bilingual word embedding"
    - "bilingual lexicon induction with semi supervision in non isometric embedding spaces"
    - "lost in evaluation misleading benchmarks for bilingual dictionary induction"
    - "unsupervised statistical machine translation"
    - "unsupervised multilingual word embeddings"
    - "learning word vectors for 157 languages"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-6 (induced by source paper "ccmatrix mining billions of high quality parallel sentences on the web"):
  with members:
    - "reducing transformer depth on demand with structured dropout"
    - "ccnet extracting high quality monolingual datasets from web crawl data"
    - "achieving human parity on automatic chinese to english news translation"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "filtering and mining parallel data in a joint multilingual space"
    - "learning word vectors for 157 languages"
    - "margin based parallel corpus mining with multilingual sentence embeddings"
    - "an empirical analysis of nmt derived interlingual embeddings and their use in parallel sentence identification"

Connected through hyperedge HE-7 (induced by source paper "gebiotoolkit automatic extraction of gender balanced multilingual corpus of wikipedia biographies"):
  with members:
    - "evaluating gender bias in machine translation"
    - "evaluating the underlying gender bias in contextualized word embeddings"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "assessing gender bias in machine translation a case study with google translate"
    - "gender bias in coreference resolution"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "reducing gender bias in abusive language detection"
    - "margin based parallel corpus mining with multilingual sentence embeddings"

Connected through hyperedge HE-8 (induced by source paper "jparacrawl a large scale web based english japanese parallel corpus"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "scaling neural machine translation"
    - "microsoft s submission to the wmt2018 news translation task how i learned to stop worrying and love the data"
    - "a call for clarity in reporting bleu scores"
    - "attention is all you need"
    - "mixed precision training"
    - "jesc japanese english subtitle corpus"

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)