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: "fine grained prediction of syntactic typology discovering latent structure with supervised learning"
  Abstract: We show how to predict the basic word-order facts of a novel language given only a corpus of part-of-speech (POS) sequences. We predict how often direct objects follow their verbs, how often adjectives follow their nouns, and in general the directionalities of all dependency relations. Such typological properties could be helpful in grammar induction. While such a problem is usually regarded as unsupervised learning, our innovation is to treat it as supervised learning, using a large collection of realistic synthetic languages as training data. The supervised learner must identify surface features of a language's POS sequence (hand-engineered or neural features) that correlate with the language's deeper structure (latent trees). In the experiment, we show: 1) Given a small set of real languages, it helps to add many synthetic languages to the training data. 2) Our system is robust even when the POS sequences include noise. 3) Our system on this task outperforms a grammar induction baseline by a large margin.

Connected through hyperedge HE-1 (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-2 (induced by source paper "phaselink a deep learning approach to seismic phase association"):
  with members:
    - "deepdetect a cascaded region based densely connected network for seismic event detection"
    - "carla an open urban driving simulator"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "image captioning with semantic attention"
    - "play and learn using video games to train computer vision models"
    - "wavenet a generative model for raw audio"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (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"
    - "the galactic dependencies treebanks getting more data by synthesizing new languages"
    - "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-4 (induced by source paper "low resource syntactic transfer with unsupervised source reordering"):
  with members:
    - "the galactic dependencies treebanks getting more data by synthesizing new languages"
    - "neural machine translation for low resource languages"
    - "a survey of word reordering in statistical machine translation computational models and language phenomena"
    - "deep biaffine attention for neural dependency parsing"
    - "cross lingual syntactic transfer with limited resources"
    - "many languages one parser"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "modeling language variation and universals a survey on typological linguistics for natural language processing"):
  with members:
    - "morphological word embeddings"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "xnli evaluating cross lingual sentence representations"
    - "a deep generative model of vowel formant typology"
    - "deep contextualized word representations"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "adversarial propagation and zero shot cross lingual transfer of word vector specialization"
    - "past present future a computational investigation of the typology of tense in 1000 languages"

Connected through hyperedge HE-6 (induced by source paper "near or far wide range zero shot cross lingual dependency parsing"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "self attention with relative position representations"
    - "unsupervised cross lingual information retrieval using monolingual data only"
    - "deep contextualized word representations"
    - "a comparison of transformer and recurrent neural networks on multilingual neural machine translation"
    - "constituency parsing with a self attentive encoder"
    - "efficient dependency guided named entity recognition"
    - "neural cross lingual coreference resolution and its application to entity linking"

Connected through hyperedge HE-7 (induced by source paper "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "constituency parsing with a self attentive encoder"
    - "efficient dependency guided named entity recognition"
    - "neural cross lingual coreference resolution and its application to entity linking"
    - "one shot neural cross lingual transfer for paradigm completion"
    - "attention is all you need"

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)