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: "sentence segmentation in narrative transcripts from neuropsychological tests using recurrent convolutional neural networks"
  Abstract: Automated discourse analysis tools based on Natural Language Processing (NLP) aiming at the diagnosis of language-impairing dementias generally extract several textual metrics of narrative transcripts. However, the absence of sentence boundary segmentation in the transcripts prevents the direct application of NLP methods which rely on these marks in order to function properly, such as taggers and parsers. We present the first steps taken towards automatic neuropsychological evaluation based on narrative discourse analysis, presenting a new automatic sentence segmentation method for impaired speech. Our model uses recurrent convolutional neural networks with prosodic, Part of Speech (PoS) features, and word embeddings. It was evaluated intrinsically on impaired, spontaneous speech as well as normal, prepared speech. The results suggest that our model is robust for impaired speech and can be used in automated discourse analysis tools to differentiate narratives produced by Mild Cognitive Impairment and healthy elderly patients.

Connected through hyperedge HE-1 (induced by source paper "pelesent cross domain polarity classification using distant supervision"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "sentiment of emojis"
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"
    - "a convolutional neural network for modelling sentences"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "recurrent convolutional neural networks for discourse compositionality"

Connected through hyperedge HE-2 (induced by source paper "enriching complex networks with word embeddings for detecting mild cognitive impairment from speech transcripts"):
  with members:
    - "authorship attribution based on life like network automata"
    - "enriching word vectors with subword information"
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "a complex network approach to stylometry"
    - "inducing language networks from continuous space word representations"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-3 (induced by source paper "evaluating word embeddings for sentence boundary detection in speech transcripts"):
  with members:
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "efficient estimation of word representations in vector space"
    - "prosody based automatic segmentation of speech into sentences and topics"

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)