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 and cross domain discourse segmentation of entire documents"
  Abstract: Discourse segmentation is a crucial step in building end-to-end discourse parsers. However, discourse segmenters only exist for a few languages and domains. Typically they only detect intra-sentential segment boundaries, assuming gold standard sentence and token segmentation, and relying on high-quality syntactic parses and rich heuristics that are not generally available across languages and domains. In this paper, we propose statistical discourse segmenters for five languages and three domains that do not rely on gold pre-annotations. We also consider the problem of learning discourse segmenters when no labeled data is available for a language. Our fully supervised system obtains 89.5% F1 for English newswire, with slight drops in performance on other domains, and we report supervised and unsupervised (cross-lingual) results for five languages in total.

Connected through hyperedge HE-1 (induced by source paper "multi task learning for argumentation mining"):
  with members:
    - "argumentation mining in user generated web discourse"
    - "lstm a search space odyssey"
    - "neural end to end learning for computational argumentation mining"
    - "multi task learning for mental health using social media text"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "attention strategies for multi source sequence to sequence learning"
    - "multitask learning for fine grained twitter sentiment analysis"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"

Connected through hyperedge HE-2 (induced by source paper "is it time to swish comparing deep learning activation functions across nlp tasks"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "self normalizing neural networks"
    - "neural end to end learning for computational argumentation mining"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "searching for activation functions"
    - "parsing argumentation structures in persuasive essays"
    - "revise saturated activation functions"

Connected through hyperedge HE-3 (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-4 (induced by source paper "multi task learning for argumentation mining in low resource settings"):
  with members:
    - "argumentation mining in user generated web discourse"
    - "neural end to end learning for computational argumentation mining"
    - "multi task learning for mental health using social media text"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "using argument based features to predict and analyse review helpfulness"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "parsing argumentation structures in persuasive essays"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"

Connected through hyperedge HE-5 (induced by source paper "cross lingual argumentation mining machine translation and a bit of projection is all you need"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "multi task learning for argumentation mining in low resource settings"
    - "neural end to end learning for computational argumentation mining"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "crowdsourcing argumentation structures in chinese hotel reviews"
    - "cross lingual character level neural morphological tagging"
    - "cross lingual models of word embeddings an empirical comparison"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-6 (induced by source paper "chinese discourse segmentation using bilingual discourse commonality"):
  with members:
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "learning based single document summarization with compression and anaphoricity constraints"
    - "multi task cross lingual sequence tagging from scratch"
    - "adversarial deep averaging networks for cross lingual sentiment classification"
    - "bidirectional lstm crf models for sequence tagging"
    - "teaching machines to read and comprehend"
    - "convolutional neural networks for sentence classification"
    - "dropout improves recurrent neural networks for handwriting recognition"

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)