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: "cross lingual rst discourse parsing"
  Abstract: Discourse parsing is an integral part of understanding information flow and argumentative structure in documents. Most previous research has focused on inducing and evaluating models from the English RST Discourse Treebank. However, discourse treebanks for other languages exist, including Spanish, German, Basque, Dutch and Brazilian Portuguese. The treebanks share the same underlying linguistic theory, but differ slightly in the way documents are annotated. In this paper, we present (a) a new discourse parser which is simpler, yet competitive (significantly better on 2/3 metrics) to state of the art for English, (b) a harmonization of discourse treebanks across languages, enabling us to present (c) what to the best of our knowledge are the first experiments on cross-lingual discourse parsing.

Connected through hyperedge HE-1 (induced by source paper "neural generative rhetorical structure parsing"):
  with members:
    - "a unified linear time framework for sentence level discourse parsing"
    - "variational smoothing in recurrent neural network language models"
    - "improving neural parsing by disentangling model combination and reranking effects"
    - "neural discourse structure for text categorization"
    - "effective inference for generative neural parsing"
    - "recurrent neural network grammars"
    - "globally normalized transition based neural networks"
    - "a latent variable recurrent neural network for discourse relation language models"

Connected through hyperedge HE-2 (induced by source paper "predicting discourse structure using distant supervision from sentiment"):
  with members:
    - "evaluating discourse in structured text representations"
    - "learning structured text representations"
    - "neural discourse structure for text categorization"
    - "multiple instance learning networks for fine grained sentiment analysis"
    - "tweetime a minimally supervised method for recognizing and normalizing time expressions in twitter"
    - "character level convolutional networks for text classification"
    - "better document level sentiment analysis from rst discourse parsing"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "a unified linear time framework for sentence level discourse parsing"):
  with members:
    - "deep contextualized word representations"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "deep biaffine attention for neural dependency parsing"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "gumdrop at the disrpt2019 shared task a model stacking approach to discourse unit segmentation and connective detection"):
  with members:
    - "infynlp at smm4h task 2 stacked ensemble of shallow convolutional neural networks for identifying personal medication intake from twitter"
    - "ncrf an open source neural sequence labeling toolkit"
    - "xgboost a scalable tree boosting system"
    - "enriching word vectors with subword information"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-5 (induced by source paper "a deep sequential model for discourse parsing on multi party dialogues"):
  with members:
    - "scidtb discourse dependency treebank for scientific abstracts"
    - "dialogue act recognition via crf attentive structured network"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "better document level sentiment analysis from rst discourse parsing"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-6 (induced by source paper "learning structured text representations"):
  with members:
    - "structured attention networks"
    - "frustratingly short attention spans in neural language modeling"
    - "neural discourse structure for text categorization"
    - "long short term memory networks for machine reading"
    - "a decomposable attention model for natural language inference"
    - "enhanced lstm for natural language inference"
    - "key value memory networks for directly reading documents"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"

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)