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 noisy channel model and a deep neural language model"
  Abstract: This paper presents a model for disfluency detection in spontaneous speech transcripts called LSTM Noisy Channel Model. The model uses a Noisy Channel Model (NCM) to generate n-best candidate disfluency analyses and a Long Short-Term Memory (LSTM) language model to score the underlying fluent sentences of each analysis. The LSTM language model scores, along with other features, are used in a MaxEnt reranker to identify the most plausible analysis. We show that using an LSTM language model in the reranking process of noisy channel disfluency model improves the state-of-the-art in disfluency detection.

Connected through hyperedge HE-1 (induced by source paper "controllable time delay transformer for real time punctuation prediction and disfluency detection"):
  with members:
    - "fast and accurate capitalization and punctuation for automatic speech recognition using transformer and chunk merging"
    - "multi task self supervised learning for disfluency detection"
    - "stacl simultaneous translation with implicit anticipation and controllable latency using prefix to prefix framework"
    - "hybrid self attention network for machine translation"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "adam a method for stochastic optimization"

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"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "attention is all you need"

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"
    - "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"
    - "disfluency detection using a bidirectional lstm"

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"
    - "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"
    - "disfluency detection using a bidirectional lstm"

Connected through hyperedge HE-5 (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"
    - "tensor fusion network for multimodal sentiment analysis"
    - "parsing speech a neural approach to integrating lexical and acoustic prosodic information"
    - "disfluency detection using a bidirectional lstm"
    - "show attend and tell neural image caption generation with visual attention"
    - "unifying visual semantic embeddings with multimodal neural language models"

Connected through hyperedge HE-6 (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"
    - "attention is all you need"
    - "a minimal span based neural constituency parser"
    - "parsing speech a neural approach to integrating lexical and acoustic prosodic information"
    - "disfluency detection using a bidirectional lstm"

Connected through hyperedge HE-7 (induced by source paper "disfluency detection using auto correlational neural networks"):
  with members:
    - "parsing speech a neural approach to integrating lexical and acoustic prosodic information"
    - "a general purpose tagger with convolutional neural networks"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "robust cross domain disfluency detection with pattern match networks"):
  with members:
    - "parsing speech a neural approach to integrating lexical and acoustic prosodic information"
    - "disfluency detection using a bidirectional lstm"

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)