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: "joint learning of correlated sequence labelling tasks using bidirectional recurrent neural networks"
  Abstract: The stream of words produced by Automatic Speech Recognition (ASR) systems is typically devoid of punctuations and formatting. Most natural language processing applications expect segmented and well-formatted texts as input, which is not available in ASR output. This paper proposes a novel technique of jointly modeling multiple correlated tasks such as punctuation and capitalization using bidirectional recurrent neural networks, which leads to improved performance for each of these tasks. This method could be extended for joint modeling of any other correlated sequence labeling tasks.

Connected through hyperedge HE-1 (induced by source paper "factored latent dynamic conditional random fields for single and multi label sequence modeling"):
  with members:
    - "context model for pedestrian intention prediction using factored latent dynamic conditional random fields"
    - "embedded state latent conditional random fields for sequence labeling"
    - "multi attention recurrent network for human communication comprehension"
    - "multi task learning for sequence tagging an empirical study"
    - "video based sign language recognition without temporal segmentation"
    - "lstm a search space odyssey"
    - "hcp a flexible cnn framework for multi label image classification"
    - "cnn rnn a unified framework for multi label image classification"

Connected through hyperedge HE-2 (induced by source paper "towards effective rebuttal listening comprehension using corpus wide claim mining"):
  with members:
    - "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
    - "argumentation mining in user generated web discourse"
    - "a recorded debating dataset"
    - "towards machine comprehension of spoken content initial toefl listening comprehension test by machine"
    - "parsing argumentation structures in persuasive essays"
    - "hierarchical attention model for improved machine comprehension of spoken content"
    - "efficient estimation of word representations in vector space"

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)