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: "real time automatic word segmentation for user generated text"
  Abstract: For readability and possibly for disambiguation, appropriate word segmentation is recommended for written text. In this paper, we propose a real-time assistive technology that utilizes an automatic segmentation. The language investigated is Korean, a head-final language with various morpho-syllabic blocks as characters. The training scheme is fully neural network-based and straightforward. Besides, we show how the proposed system can be utilized in a web-based real-time revision for a user-generated text. With qualitative and quantitative comparison with widely used text processing toolkits, we show the reliability of the proposed system and how it fits with conversation-style and non-canonical texts. The demonstration is available online.

Connected through hyperedge HE-1 (induced by source paper "investigating an effective character level embedding in korean sentence classification"):
  with members:
    - "speech intention understanding in a head final language a disambiguation utilizing intonation dependency"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "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 "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"
    - "speech intention understanding in a head final language a disambiguation utilizing intonation dependency"
    - "a structured self attentive sentence embedding"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-3 (induced by source paper "speech intention understanding in a head final language a disambiguation utilizing intonation dependency"):
  with members:
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "a structured self attentive sentence embedding"
    - "tweet acts a speech act classifier for twitter"
    - "enriching word vectors with subword information"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

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)