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 unified linear time framework for sentence level discourse parsing"
  Abstract: We propose an efficient neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory (RST). Our framework comprises a discourse segmenter to identify the elementary discourse units (EDU) in a text, and a discourse parser that constructs a discourse tree in a top-down fashion. Both the segmenter and the parser are based on Pointer Networks and operate in linear time. Our segmenter yields an $F_1$ score of 95.4, and our parser achieves an $F_1$ score of 81.7 on the aggregated labeled (relation) metric, surpassing previous approaches by a good margin and approaching human agreement on both tasks (98.3 and 83.0 $F_1$).

Connected through hyperedge HE-1 (induced by source paper "syntactically look ahead attention network for sentence compression"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural machine translation with source side latent graph parsing"
    - "dependency parsing as head selection"
    - "improving sentence compression by learning to predict gaze"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

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"
    - "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"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-3 (induced by source paper "neural generative rhetorical structure parsing"):
  with members:
    - "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"
    - "a latent variable recurrent neural network for discourse relation language models"

Connected through hyperedge HE-4 (induced by source paper "hierarchical pointer net parsing"):
  with members:
    - "deep contextualized word representations"
    - "deep biaffine attention for neural dependency parsing"
    - "globally normalized transition based neural networks"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "transition based dependency parsing with stack long short term memory"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)