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: "unsupervised neural word segmentation for chinese via segmental language modeling"
  Abstract: Previous traditional approaches to unsupervised Chinese word segmentation (CWS) can be roughly classified into discriminative and generative models. The former uses the carefully designed goodness measures for candidate segmentation, while the latter focuses on finding the optimal segmentation of the highest generative probability. However, while there exists a trivial way to extend the discriminative models into neural version by using neural language models, those of generative ones are non-trivial. In this paper, we propose the segmental language models (SLMs) for CWS. Our approach explicitly focuses on the segmental nature of Chinese, as well as preserves several properties of language models. In SLMs, a context encoder encodes the previous context and a segment decoder generates each segment incrementally. As far as we know, we are the first to propose a neural model for unsupervised CWS and achieve competitive performance to the state-of-the-art statistical models on four different datasets from SIGHAN 2005 bakeoff.

Connected through hyperedge HE-1 (induced by source paper "learning to discover ground and use words with segmental neural language models"):
  with members:
    - "revisiting the hierarchical multiscale lstm"
    - "spell once summon anywhere a two level open vocabulary language model"
    - "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 "unsupervised word discovery with segmental neural language models"):
  with members:
    - "spell once summon anywhere a two level open vocabulary language model"
    - "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"
    - "adam a method for stochastic optimization"

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)