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: "speech intention understanding in a head final language a disambiguation utilizing intonation dependency"
  Abstract: For a large portion of real-life utterances, the intention cannot be solely decided by either their semantics or syntax. Although all the socio-linguistic and pragmatic information cannot be digitized, at least phonetic features are indispensable in understanding the spoken language. Especially in head-final languages such as Korean, sentence-final intonation has great importance in identifying the speaker's intention. This paper suggests a system which identifies the intention of an utterance, given its acoustic feature and text. The proposed multi-stage classification system decides whether given utterance is a fragment, statement, question, command, or a rhetorical one, utilizing the intonation-dependency coming from head-finality. Based on an intuitive understanding of Korean language which is engaged in data annotation, we construct a network identifying the intention of a speech and validate its utility with sample sentences. The system, if combined with the speech recognizers, is expected to be flexibly inserted into various language understanding modules.

Connected through hyperedge HE-1 (induced by source paper "investigating an effective character level embedding in korean sentence classification"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "real time automatic word segmentation for user generated text"
    - "attention is all you need"
    - "a sub character architecture for korean language processing"
    - "which encoding is the best for text classification in chinese english japanese and korean"
    - "a structured self attentive sentence embedding"
    - "tweet2vec character based distributed representations for social media"
    - "neural language correction with character based attention"

Connected through hyperedge HE-2 (induced by source paper "machines getting with the program understanding intent arguments of non canonical directives"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "disambiguating speech intention via audio text co attention framework a case of prosody semantics interface"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "extracting arguments from korean question and command an annotated corpus for structured paraphrasing"
    - "learning to paraphrase for question answering"
    - "attention is all you need"
    - "cross domain semantic parsing via paraphrasing"

Connected through hyperedge HE-3 (induced by source paper "disambiguating speech intention via audio text co attention framework a case of prosody semantics interface"):
  with members:
    - "investigating an effective character level embedding in korean sentence classification"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "predicting prosodic prominence from text with pre trained contextualized word representations"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "real time automatic word segmentation for user generated text"
    - "a structured self attentive sentence embedding"
    - "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)