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: "robust cross domain disfluency detection with pattern match networks"
  Abstract: In this paper we introduce a novel pattern match neural network architecture that uses neighbor similarity scores as features, eliminating the need for feature engineering in a disfluency detection task. We evaluate the approach in disfluency detection for four different speech genres, showing that the approach is as effective as hand-engineered pattern match features when used on in-domain data and achieves superior performance in cross-domain scenarios.

Connected through hyperedge HE-1 (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"
    - "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"
    - "attention is all you need"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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-3 (induced by source paper "disfluencies and human speech transcription errors"):
  with members:
    - "giving attention to the unexpected using prosody innovations in disfluency detection"
    - "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"
    - "disfluency detection using a bidirectional lstm"
    - "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)