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: "a pdtb styled end to end discourse parser"
  Abstract: We have developed a full discourse parser in the Penn Discourse Treebank (PDTB) style. Our trained parser first identifies all discourse and non-discourse relations, locates and labels their arguments, and then classifies their relation types. When appropriate, the attribution spans to these relations are also determined. We present a comprehensive evaluation from both component-wise and error-cascading perspectives.

Connected through hyperedge HE-1 (induced by source paper "discourse level factors for sentence deletion in text simplification"):
  with members:
    - "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "a word complexity lexicon and a neural readability ranking model for lexical simplification"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "sentence simplification with memory augmented neural networks"
    - "simple and effective text simplification using semantic and neural methods"
    - "sentence simplification with deep reinforcement learning"
    - "learning based single document summarization with compression and anaphoricity constraints"

Connected through hyperedge HE-2 (induced by source paper "argumentative relation classification as plausibility ranking"):
  with members:
    - "dissecting content and context in argumentative relation analysis"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self normalizing neural networks"
    - "a mention ranking model for abstract anaphora resolution"
    - "attention is all you need"
    - "parsing argumentation structures in persuasive essays"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "dissecting content and context in argumentative relation analysis"):
  with members:
    - "parsing argumentation structures in persuasive essays"
    - "yara parser a fast and accurate dependency parser"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "a hybrid framework for topic structure using laughter occurrences"):
  with members:
    - "an analysis of rhythmic staccato vocalization based on frequency demodulation for laughter detection in conversational meetings"
    - "advances in domain independent linear text segmentation"

Connected through hyperedge HE-5 (induced by source paper "improving implicit discourse relation classification by modeling inter dependencies of discourse units in a paragraph"):
  with members:
    - "spherical paragraph model"
    - "deconvolutional paragraph representation learning"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "a latent variable recurrent neural network for discourse relation language models"
    - "neural network models for implicit discourse relation classification in english and chinese without surface features"
    - "recognizing implicit discourse relations via repeated reading neural networks with multi level attention"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "discourse in multimedia a case study in information extraction"):
  with members:
    - "rhetorical relations for information retrieval"
    - "type and content driven synthesis of sql queries from natural language"
    - "a syntactic neural model for general purpose code generation"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "latent predictor networks for code generation"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"
    - "learning dependency based compositional semantics"
    - "pattern matching and discourse processing in information extraction from japanese text"

Connected through hyperedge HE-7 (induced by source paper "causal explanation analysis on social media"):
  with members:
    - "neural discourse structure for text categorization"
    - "better document level sentiment analysis from rst discourse parsing"
    - "towards universal paraphrastic sentence embeddings"
    - "adam a method for stochastic optimization"
    - "nrc canada building the state of the art in sentiment analysis of tweets"

Connected through hyperedge HE-8 (induced by source paper "acquiring annotated data with cross lingual explicitation for implicit discourse relation classification"):
  with members:
    - "learning to explicitate connectives with seq2seq network for implicit discourse relation classification"
    - "a latent variable recurrent neural network for discourse relation language models"
    - "one vector is not enough entity augmented distributional semantics for discourse relations"

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)