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: "an empirical analysis of nmt derived interlingual embeddings and their use in parallel sentence identification"
  Abstract: End-to-end neural machine translation has overtaken statistical machine translation in terms of translation quality for some language pairs, specially those with large amounts of parallel data. Besides this palpable improvement, neural networks provide several new properties. A single system can be trained to translate between many languages at almost no additional cost other than training time. Furthermore, internal representations learned by the network serve as a new semantic representation of words—or sentences—which, unlike standard word embeddings, are learned in an essentially bilingual or even multilingual context. In view of these properties, the contribution of the present paper is twofold. First, we systematically study the neural machine translation (NMT) context vectors, i.e., output of the encoder, and their power as an interlingua representation of a sentence. We assess their quality and effectiveness by measuring similarities across translations, as well as semantically related and semantically unrelated sentence pairs. Second, as extrinsic evaluation of the first point, we identify parallel sentences in comparable corpora, obtaining an   $F_1=98.2\%$   on data from a shared task when using only NMT context vectors. Using context vectors jointly with similarity measures    $F_1$   reaches    $98.9\%$  .

Connected through hyperedge HE-1 (induced by source paper "massively multilingual document alignment with cross lingual sentence mover s distance"):
  with members:
    - "a massive collection of cross lingual web document pairs"
    - "hierarchical document encoder for parallel corpus mining"
    - "improving multilingual sentence embedding using bi directional dual encoder with additive margin softmax"
    - "cross lingual language model pretraining"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "cross lingual document retrieval using regularized wasserstein distance"
    - "linear complexity relaxed word mover s distance with gpu acceleration"

Connected through hyperedge HE-2 (induced by source paper "a bilingual generative transformer for semantic sentence embedding"):
  with members:
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "simple and effective paraphrastic similarity from parallel translations"
    - "beyond bleu training neural machine translation with semantic similarity"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "filtering and mining parallel data in a joint multilingual space"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"

Connected through hyperedge HE-3 (induced by source paper "simple and effective paraphrastic similarity from parallel translations"):
  with members:
    - "parabank monolingual bitext generation and sentential paraphrasing via lexically constrained neural machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "filtering and mining parallel data in a joint multilingual space"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "deep contextualized word representations"
    - "extracting parallel sentences with bidirectional recurrent neural networks to improve machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "towards interlingua neural machine translation"):
  with members:
    - "the missing ingredient in zero shot neural machine translation"
    - "unsupervised statistical machine translation"
    - "zero shot cross lingual classification using multilingual neural machine translation"
    - "xnli evaluating cross lingual sentence representations"
    - "fast decoding in sequence models using discrete latent variables"
    - "a neural interlingua for multilingual machine translation"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-5 (induced by source paper "xcmrc evaluating cross lingual machine reading comprehension"):
  with members:
    - "a corpus for multilingual document classification in eight languages"
    - "xnli evaluating cross lingual sentence representations"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"

Connected through hyperedge HE-6 (induced by source paper "learning bilingual sentence embeddings via autoencoding and computing similarities with a multilayer perceptron"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "the missing ingredient in zero shot neural machine translation"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "filtering and mining parallel data in a joint multilingual space"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "dual conditional cross entropy filtering of noisy parallel corpora"
    - "margin based parallel corpus mining with multilingual sentence embeddings"
    - "learning joint multilingual sentence representations with neural machine translation"

Connected through hyperedge HE-7 (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"
    - "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "achieving human parity on automatic chinese to english news translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "filtering and mining parallel data in a joint multilingual space"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "margin based parallel corpus mining with multilingual sentence embeddings"

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)