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: "disambiguating speech intention via audio text co attention framework a case of prosody semantics interface"
  Abstract: Understanding the intention of an utterance is challenging for some prosody-sensitive cases, especially when it is in the written form. The main concern is to detect the directivity or rhetoricalness of an utterance and to distinguish the type of question. Since it is inevitable to face both the issues regarding prosody and semantics, the identification is expected to benefit from the observations of human language processing mechanism. In this paper, we combat the task with attentive recurrent neural networks that exploit acoustic and textual features, using a manually created speech corpus that incorporates only the syntactically ambiguous utterances which require prosody for disambiguation. We found out that co-attention frameworks on audio-text data, namely multi-hop attention and cross-attention, can perform better than previously suggested speech-based/text-aided networks. By this, we infer that understanding the genuine intention of the ambiguous utterances incorporates recognizing the interaction between auditory and linguistic processes.

Connected through hyperedge HE-1 (induced by source paper "machines getting with the program understanding intent arguments of non canonical directives"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "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"
    - "speech intention understanding in a head final language a disambiguation utilizing intonation dependency"
    - "learning to paraphrase for question answering"
    - "attention is all you need"
    - "cross domain semantic parsing via paraphrasing"

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)