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: "disfluency detection using a bidirectional lstm"
  Abstract: We introduce a new approach for disfluency detection using a Bidirectional Long-Short Term Memory neural network (BLSTM). In addition to the word sequence, the model takes as input pattern match features that were developed to reduce sensitivity to vocabulary size in training, which lead to improved performance over the word sequence alone. The BLSTM takes advantage of explicit repair states in addition to the standard reparandum states. The final output leverages integer linear programming to incorporate constraints of disfluency structure. In experiments on the Switchboard corpus, the model achieves state-of-the-art performance for both the standard disfluency detection task and the correction detection task. Analysis shows that the model has better detection of non-repetition disfluencies, which tend to be much harder to detect.

Connected through hyperedge HE-1 (induced by source paper "identification of primary and collateral tracks in stuttered speech"):
  with members:
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "parsing speech a neural approach to integrating lexical and acoustic prosodic information"
    - "imbalanced learn a python toolbox to tackle the curse of imbalanced datasets in machine learning"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "multi task self supervised learning for disfluency detection"):
  with members:
    - "giving attention to the unexpected using prosody innovations in disfluency detection"
    - "robust cross domain disfluency detection with pattern match networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "disfluency detection using auto correlational neural networks"
    - "disfluency detection using a noisy channel model and a deep neural language model"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "identifying beneficial task relations for multi task learning in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "dolphin a verbal fluency evaluation system for elementary education"):
  with members:
    - "automatic short answer grading via multiway attention networks"
    - "a multimodal alerting system for online class quality assurance"
    - "learning effective embeddings from crowdsourced labels an educational case study"
    - "disfluency detection using a noisy channel model and a deep neural language model"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "learning to compare relation network for few shot learning"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-4 (induced by source paper "dolphin a spoken language proficiency assessment system for elementary education"):
  with members:
    - "automatic short answer grading via multiway attention networks"
    - "a multimodal alerting system for online class quality assurance"
    - "learning effective embeddings from crowdsourced labels an educational case study"
    - "disfluency detection using a noisy channel model and a deep neural language model"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "learning to compare relation network for few shot learning"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-5 (induced by source paper "fluent translations from disfluent speech in end to end speech translation"):
  with members:
    - "low resource speech to text translation"
    - "xnmt the extensible neural machine translation toolkit"
    - "towards fluent translations from disfluent speech"
    - "self attentional acoustic models"
    - "sequence to sequence models can directly translate foreign speech"
    - "improving lexical choice in neural machine translation"
    - "very deep convolutional networks for end to end speech recognition"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "giving attention to the unexpected using prosody innovations in disfluency detection"):
  with members:
    - "multi attention recurrent network for human communication comprehension"
    - "robust cross domain disfluency detection with pattern match networks"
    - "disfluency detection using auto correlational neural networks"
    - "disfluency detection using a noisy channel model and a deep neural language model"
    - "tensor fusion network for multimodal sentiment analysis"
    - "parsing speech a neural approach to integrating lexical and acoustic prosodic information"
    - "show attend and tell neural image caption generation with visual attention"
    - "unifying visual semantic embeddings with multimodal neural language models"

Connected through hyperedge HE-7 (induced by source paper "neural constituency parsing of speech transcripts"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "constituency parsing with a self attentive encoder"
    - "disfluency detection using a noisy channel model and a deep neural language model"
    - "attention is all you need"
    - "a minimal span based neural constituency parser"
    - "parsing speech a neural approach to integrating lexical and acoustic prosodic information"

Connected through hyperedge HE-8 (induced by source paper "disfluencies and human speech transcription errors"):
  with members:
    - "giving attention to the unexpected using prosody innovations in disfluency detection"
    - "robust cross domain disfluency detection with pattern match networks"
    - "english conversational telephone speech recognition by humans and machines"
    - "parsing speech a neural approach to integrating lexical and acoustic prosodic information"
    - "comparing human and machine errors in conversational speech transcription"
    - "achieving human parity in conversational speech recognition"

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)