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: "neural discourse structure for text categorization"
  Abstract: We show that discourse structure, as defined by Rhetorical Structure Theory and provided by an existing discourse parser, benefits text categorization. Our approach uses a recursive neural network and a newly proposed attention mechanism to compute a representation of the text that focuses on salient content, from the perspective of both RST and the task. Experiments consider variants of the approach and illustrate its strengths and weaknesses.

Connected through hyperedge HE-1 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization"):
  with members:
    - "evaluating discourse in structured text representations"
    - "unsupervised neural single document summarization of reviews via learning latent discourse structure and its ranking"
    - "text summarization with pretrained encoders"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "toward abstractive summarization using semantic representations"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "structure infused copy mechanisms for abstractive summarization"

Connected through hyperedge HE-2 (induced by source paper "evaluation benchmarks and learning criteria for discourse aware sentence representations"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "enteval a holistic evaluation benchmark for entity representations"
    - "a unified linear time framework for sentence level discourse parsing"
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "evaluating discourse in structured text representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "linguistic knowledge and transferability of contextual representations"
    - "superglue a stickier benchmark for general purpose language understanding systems"

Connected through hyperedge HE-3 (induced by source paper "evaluation benchmarks and learning criteriafor discourse aware sentence representations"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "enteval a holistic evaluation benchmark for entity representations"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "linguistic knowledge and transferability of contextual representations"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"
    - "discourse aware neural rewards for coherent text generation"
    - "collecting diverse natural language inference problems for sentence representation evaluation"

Connected through hyperedge HE-4 (induced by source paper "is attention interpretable"):
  with members:
    - "attention is not explanation"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "before name calling dynamics and triggers of ad hominem fallacies in web argumentation"
    - "sanity checks for saliency maps"
    - "learning structured text representations"
    - "satirical news detection and analysis using attention mechanism and linguistic features"
    - "attention is all you need"
    - "spine sparse interpretable neural embeddings"

Connected through hyperedge HE-5 (induced by source paper "unsupervised neural single document summarization of reviews via learning latent discourse structure and its ranking"):
  with members:
    - "a hierarchical end to end model for jointly improving text summarization and sentiment classification"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "meansum a neural model for unsupervised multi document abstractive summarization"
    - "structured attention networks"
    - "learning structured text representations"
    - "get to the point summarization with pointer generator networks"
    - "a deep reinforced model for abstractive summarization"
    - "neural network based abstract generation for opinions and arguments"

Connected through hyperedge HE-6 (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"
    - "cross lingual rst discourse parsing"
    - "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-7 (induced by source paper "knowledge guided unsupervised rhetorical parsing for text summarization"):
  with members:
    - "sentiment analysis based on rhetorical structure theory learning deep neural networks from discourse trees"
    - "global encoding for abstractive summarization"
    - "learning to extract coherent summary via deep reinforcement learning"
    - "adapting the neural encoder decoder framework from single to multi document summarization"
    - "neural document summarization by jointly learning to score and select sentences"
    - "unsupervised machine translation using monolingual corpora only"
    - "get to the point summarization with pointer generator networks"
    - "neural summarization by extracting sentences and words"

Connected through hyperedge HE-8 (induced by source paper "mapping dis information flow about the mh17 plane crash"):
  with members:
    - "universal dependency parsing from scratch"
    - "issue framing in online discussion fora"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "junk news on military affairs and national security social media disinformation campaigns against us military personnel and veterans"
    - "framing and agenda setting in russian news a computational analysis of intricate political strategies"
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "tweet2vec character based distributed representations for social media"
    - "enriching word vectors with subword information"

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)