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: "revisiting the hierarchical multiscale lstm"
  Abstract: Hierarchical Multiscale LSTM (Chung et al., 2016a) is a state-of-the-art language model that learns interpretable structure from character-level input. Such models can provide fertile ground for (cognitive) computational linguistics studies. However, the high complexity of the architecture, training procedure and implementations might hinder its applicability. We provide a detailed reproduction and ablation study of the architecture, shedding light on some of the potential caveats of re-purposing complex deep-learning architectures. We further show that simplifying certain aspects of the architecture can in fact improve its performance. We also investigate the linguistic units (segments) learned by various levels of the model, and argue that their quality does not correlate with the overall performance of the model on language modeling.

Connected through hyperedge HE-1 (induced by source paper "learning to discover ground and use words with segmental neural language models"):
  with members:
    - "spell once summon anywhere a two level open vocabulary language model"
    - "unsupervised neural word segmentation for chinese via segmental language modeling"
    - "on the state of the art of evaluation in neural language models"
    - "representations of language in a model of visually grounded speech signal"
    - "learning to create and reuse words in open vocabulary neural language modeling"
    - "neural language modeling by jointly learning syntax and lexicon"
    - "from phonemes to images levels of representation in a recurrent neural model of visually grounded language learning"
    - "hierarchical multiscale recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "indicatements that character language models learn english morpho syntactic units and regularities"):
  with members:
    - "sequence to sequence models can directly translate foreign speech"
    - "from characters to words to in between do we capture morphology"
    - "representation of linguistic form and function in recurrent neural networks"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "character aware neural language models"

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)