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: "filtering and mining parallel data in a joint multilingual space"
  Abstract: We learn a joint multilingual sentence embedding and use the distance between sentences in different languages to filter noisy parallel data and to mine for parallel data in large news collections. We are able to improve a competitive baseline on the WMT'14 English to German task by 0.3 BLEU by filtering out 25% of the training data. The same approach is used to mine additional bitexts for the WMT'14 system and to obtain competitive results on the BUCC shared task to identify parallel sentences in comparable corpora. The approach is generic, it can be applied to many language pairs and it is independent of the architecture of the machine translation system.

Connected through hyperedge HE-1 (induced by source paper "contextual lensing of universal sentence representations"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "ccmatrix mining billions of high quality parallel sentences on the web"
    - "parameter efficient transfer learning for nlp"
    - "unsupervised cross lingual representation learning at scale"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "learning to few shot learn across diverse natural language classification tasks"

Connected through hyperedge HE-2 (induced by source paper "semantic drift in multilingual representations"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "what do language representations really represent"
    - "blackbox meets blackbox representational similarity and stability analysis of neural language models and brains"
    - "xnli evaluating cross lingual sentence representations"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "multimodal grounding for language processing"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (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"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (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"
    - "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"
    - "unsupervised learning of sentence embeddings using compositional n gram features"

Connected through hyperedge HE-5 (induced by source paper "pivot based transfer learning for neural machine translation between non english languages"):
  with members:
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "massively multilingual neural machine translation"
    - "trivial transfer learning for low resource neural machine translation"
    - "triangular architecture for rare language translation"
    - "improving zero shot translation of low resource languages"
    - "a neural interlingua for multilingual machine translation"
    - "zero shot dual machine translation"
    - "understanding back translation at scale"

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"
    - "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"
    - "an empirical analysis of nmt derived interlingual embeddings and their use in parallel sentence identification"

Connected through hyperedge HE-7 (induced by source paper "exploring multilingual syntactic sentence representations"):
  with members:
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "assessing bert s syntactic abilities"
    - "universal dependency parsing from scratch"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (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"
    - "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"

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)