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: "learning language representations for typology prediction"
  Abstract: One central mystery of neural NLP is what neural models "know" about their subject matter. When a neural machine translation system learns to translate from one language to another, does it learn the syntax or semantics of the languages? Can this knowledge be extracted from the system to fill holes in human scientific knowledge? Existing typological databases contain relatively full feature specifications for only a few hundred languages. Exploiting the existence of parallel texts in more than a thousand languages, we build a massive many-to-one neural machine translation (NMT) system from 1017 languages into English, and use this to predict information missing from typological databases. Experiments show that the proposed method is able to infer not only syntactic, but also phonological and phonetic inventory features, and improves over a baseline that has access to information about the languages' geographic and phylogenetic neighbors.

Connected through hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity"):
  with members:
    - "multilingual alignment of contextual word representations"
    - "emerging cross lingual structure in pretrained language models"
    - "an empirical study on post processing methods for word embeddings"
    - "informing unsupervised pretraining with external linguistic knowledge"
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "word similarity datasets for thai construction and evaluation"
    - "paws paraphrase adversaries from word scrambling"
    - "cross lingual ability of multilingual bert an empirical study"

Connected through hyperedge HE-2 (induced by source paper "parameter space factorization for zero shot learning across tasks and languages"):
  with members:
    - "the case for bayesian deep learning"
    - "learning and evaluating general linguistic intelligence"
    - "what do language representations really represent"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "mlqa evaluating cross lingual extractive question answering"
    - "how multilingual is multilingual bert"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "cross lingual language model pretraining"

Connected through hyperedge HE-3 (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"
    - "emerging language spaces learned from massively multilingual corpora"

Connected through hyperedge HE-4 (induced by source paper "multilingual neural machine translation with language clustering"):
  with members:
    - "unsupervised pivot translation for distant languages"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"
    - "language graph distillation for low resource machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "beyond error propagation in neural machine translation characteristics of language also matter"
    - "a neural interlingua for multilingual machine translation"
    - "contextual parameter generation for universal neural machine translation"

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"
    - "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"
    - "lost in evaluation misleading benchmarks for bilingual dictionary induction"
    - "unsupervised statistical machine translation"
    - "unsupervised multilingual word embeddings"
    - "learning word vectors for 157 languages"

Connected through hyperedge HE-6 (induced by source paper "uncovering probabilistic implications in typological knowledge bases"):
  with members:
    - "a probabilistic generative model of linguistic typology"
    - "semantic drift in multilingual representations"
    - "parameter sharing between dependency parsers for related languages"
    - "modeling language variation and universals a survey on typological linguistics for natural language processing"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "a deep generative model of vowel formant typology"
    - "past present future a computational investigation of the typology of tense in 1000 languages"
    - "the galactic dependencies treebanks getting more data by synthesizing new languages"

Connected through hyperedge HE-7 (induced by source paper "a probabilistic generative model of linguistic typology"):
  with members:
    - "what do language representations really represent"
    - "semantic drift in multilingual representations"
    - "modeling language variation and universals a survey on typological linguistics for natural language processing"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "past present future a computational investigation of the typology of tense in 1000 languages"
    - "probabilistic typology deep generative models of vowel inventories"
    - "tracking typological traits of uralic languages in distributed language representations"
    - "continuous multilinguality with language vectors"

Connected through hyperedge HE-8 (induced by source paper "what do language representations really represent"):
  with members:
    - "modeling language variation and universals a survey on typological linguistics for natural language processing"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "found in translation reconstructing phylogenetic language trees from translations"
    - "probabilistic typology deep generative models of vowel inventories"
    - "continuous multilinguality with language vectors"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "many languages one parser"
    - "grammar as a foreign language"

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)