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: "the galactic dependencies treebanks getting more data by synthesizing new languages"
  Abstract: We release Galactic Dependencies 1.0---a large set of synthetic languages not found on Earth, but annotated in Universal Dependencies format. This new resource aims to provide training and development data for NLP methods that aim to adapt to unfamiliar languages. Each synthetic treebank is produced from a real treebank by stochastically permuting the dependents of nouns and/or verbs to match the word order of other real languages. We discuss the usefulness, realism, parsability, perplexity, and diversity of the synthetic languages. As a simple demonstration of the use of Galactic Dependencies, we consider single-source transfer, which attempts to parse a real target language using a parser trained on a "nearby" source language. We find that including synthetic source languages somewhat increases the diversity of the source pool, which significantly improves results for most target languages.

Connected through hyperedge HE-1 (induced by source paper "improving cross lingual model transfer by chunking"):
  with members:
    - "target language aware constrained inference for cross lingual dependency parsing"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"

Connected through hyperedge HE-2 (induced by source paper "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"):
  with members:
    - "context aware cross lingual mapping"
    - "linguistic knowledge and transferability of contextual representations"
    - "gromov wasserstein alignment of word embedding spaces"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "parameter sharing between dependency parsers for related languages"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "low resource parsing with crosslingual contextualized representations"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "polyglot contextual representations improve crosslingual transfer"
    - "linguistic knowledge and transferability of contextual representations"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "parameter sharing between dependency parsers for related languages"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (induced by source paper "distilling task specific knowledge from bert into simple neural networks"):
  with members:
    - "assessing bert s syntactic abilities"
    - "training and inference with integers in deep neural networks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "flops as a direct optimization objective for learning sparse neural networks"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-5 (induced by source paper "studying the inductive biases of rnns with synthetic variations of natural languages"):
  with members:
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "what do rnn language models learn about filler gap dependencies"
    - "assessing composition in sentence vector representations"
    - "rnns as psycholinguistic subjects syntactic state and grammatical dependency"
    - "do language models understand anything on the ability of lstms to understand negative polarity items"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "are all languages equally hard to language model"

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"
    - "learning language representations for typology prediction"

Connected through hyperedge HE-7 (induced by source paper "working hard or hardly working challenges of integrating typology into neural dependency parsers"):
  with members:
    - "low resource syntactic transfer with unsupervised source reordering"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"
    - "fine grained prediction of syntactic typology discovering latent structure with supervised learning"
    - "survey on the use of typological information in natural language processing"
    - "deep biaffine attention for neural dependency parsing"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "many languages one parser"

Connected through hyperedge HE-8 (induced by source paper "a generative model for punctuation in dependency trees"):
  with members:
    - "recurrent neural network grammars"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "unsupervised neural hidden markov models"
    - "yara parser a fast and accurate dependency parser"
    - "improved semantic representations from tree structured long short term memory networks"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"
    - "thumbs up sentiment classification using machine learning techniques"

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)