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: "emerging language spaces learned from massively multilingual corpora"
  Abstract: Translations capture important information about languages that can be used as implicit supervision in learning linguistic properties and semantic representations. In an information-centric view, translated texts may be considered as semantic mirrors of the original text and the significant variations that we can observe across various languages can be used to disambiguate a given expression using the linguistic signal that is grounded in translation. Parallel corpora consisting of massive amounts of human translations with a large linguistic variation can be applied to increase abstractions and we propose the use of highly multilingual machine translation models to find language-independent meaning representations. Our initial experiments show that neural machine translation models can indeed learn in such a setup and we can show that the learning algorithm picks up information about the relation between languages in order to optimize transfer leaning with shared parameters. The model creates a continuous language space that represents relationships in terms of geometric distances, which we can visualize to illustrate how languages cluster according to language families and groups. Does this open the door for new ideas of data-driven language typology with promising models and techniques in empirical cross-linguistic research?

Connected through hyperedge HE-1 (induced by source paper "investigating multilingual nmt representations at scale"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "consistency by agreement in zero shot neural machine translation"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "multilingual neural machine translation with language clustering"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"
    - "the missing ingredient in zero shot neural machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-2 (induced by source paper "massively multilingual neural machine translation"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "achieving human parity on automatic chinese to english news translation"
    - "zero shot cross lingual classification using multilingual neural machine translation"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "universal neural machine translation for extremely low resource languages"
    - "parameter sharing methods for multilingual self attentional translation models"
    - "scaling neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "rapid adaptation of neural machine translation to new languages"):
  with members:
    - "xnmt the extensible neural machine translation toolkit"
    - "universal neural machine translation for extremely low resource languages"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised machine translation using monolingual corpora only"
    - "stronger baselines for trustable results in neural machine translation"
    - "unsupervised neural machine translation"
    - "learning language representations for typology prediction"

Connected through hyperedge HE-4 (induced by source paper "generating contradictory neutral and entailing sentences"):
  with members:
    - "toward controlled generation of text"
    - "generating sentences by editing prototypes"
    - "natural language inference over interaction space"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "a deep generative framework for paraphrase generation"
    - "enhanced lstm for natural language inference"

Connected through hyperedge HE-5 (induced by source paper "embedding learning through multilingual concept induction"):
  with members:
    - "graph based bilingual word embedding for statistical machine translation"
    - "past present future a computational investigation of the typology of tense in 1000 languages"
    - "learning language representations for typology prediction"
    - "cross lingual models of word embeddings an empirical comparison"
    - "continuous multilinguality with language vectors"
    - "a strong baseline for learning cross lingual word embeddings from sentence alignments"
    - "massively multilingual word embeddings"
    - "multilingual language processing from bytes"

Connected through hyperedge HE-6 (induced by source paper "challenges of language technologies for the indigenous languages of the americas"):
  with members:
    - "from characters to words to in between do we capture morphology"
    - "survey on the use of typological information in natural language processing"
    - "unsupervised discovery of morphemes"

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)