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: "better document level sentiment analysis from rst discourse parsing"
  Abstract: Discourse structure is the hidden link between surface features and document-level properties, such as sentiment polarity. We show that the discourse analyses produced by Rhetorical Structure Theory (RST) parsers can improve document-level sentiment analysis, via composition of local information up the discourse tree. First, we show that reweighting discourse units according to their position in a dependency representation of the rhetorical structure can yield substantial improvements on lexicon-based sentiment analysis. Next, we present a recursive neural network over the RST structure, which offers significant improvements over classification-based methods.

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 "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"
    - "neural discourse structure for text categorization"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-3 (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"
    - "cross lingual rst discourse parsing"
    - "effective inference for generative neural parsing"
    - "recurrent neural network grammars"
    - "globally normalized transition based neural networks"

Connected through hyperedge HE-4 (induced by source paper "sentiment analysis based on rhetorical structure theory learning deep neural networks from discourse trees"):
  with members:
    - "deep learning in business analytics and operations research models applications and managerial implications"
    - "deep learning for affective computing text based emotion recognition in decision support"
    - "learning structured text representations"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "neural discourse structure for text categorization"
    - "representation learning for very short texts using weighted word embedding aggregation"
    - "improved semantic representations from tree structured long short term memory networks"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"

Connected through hyperedge HE-5 (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"
    - "cross lingual rst discourse parsing"
    - "tweetime a minimally supervised method for recognizing and normalizing time expressions in twitter"
    - "character level convolutional networks for text classification"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "learning hierarchical discourse level structure for fake news detection"):
  with members:
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "structured attention networks"
    - "learning structured text representations"
    - "beyond news contents the role of social context for fake news detection"
    - "empirical evaluation of rectified activations in convolutional network"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-7 (induced by source paper "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"):
  with members:
    - "multiple instance learning networks for fine grained sentiment analysis"
    - "best worst scaling more reliable than rating scales a case study on sentiment intensity annotation"
    - "graph based neural multi document summarization"
    - "neural network based abstract generation for opinions and arguments"
    - "unsupervised word and dependency path embeddings for aspect term extraction"
    - "image based recommendations on styles and substitutes"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "causal explanation analysis on social media"):
  with members:
    - "neural discourse structure for text categorization"
    - "towards universal paraphrastic sentence embeddings"
    - "adam a method for stochastic optimization"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "a pdtb styled end to end discourse parser"

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)