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: "strongly incremental repair detection"
  Abstract: We present STIR (STrongly Incremental Repair detection), a system that detects speech repairs and edit terms on transcripts incrementally with minimal latency. STIR uses information-theoretic measures from n-gram models as its principal decision features in a pipeline of classifiers detecting the different stages of repairs. Results on the Switchboard disfluency tagged corpus show utterance-final accuracy on a par with state-of-the-art incremental repair detection methods, but with better incremental accuracy, faster time-to-detection and less computational overhead. We evaluate its performance using incremental metrics and propose new repair processing evaluation standards.

Connected through hyperedge HE-1 (induced by source paper "increase apparent public speaking fluency by speech augmentation"):
  with members:
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "disfluency detection using a bidirectional lstm"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-2 (induced by source paper "analysing the potential of seq to seq models for incremental interpretation in task oriented dialogue"):
  with members:
    - "challenging neural dialogue models with natural data memory networks fail on incremental phenomena"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"
    - "bootstrapping incremental dialogue systems from minimal data the generalisation power of dialogue grammars"
    - "learning end to end goal oriented dialog"
    - "end to end memory networks"
    - "a neural conversational model"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "incremental natural language processing challenges strategies and evaluation"):
  with members:
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"
    - "learning to translate in real time with neural machine translation"
    - "can neural machine translation do simultaneous translation"
    - "an incremental parser for abstract meaning representation"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "transition based dependency parsing with stack long short term memory"
    - "neural machine translation by jointly learning to align and translate"

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)