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: "diachronic topics in new high german poetry"
  Abstract: Statistical topic models are increasingly and popularly used by Digital Humanities scholars to perform distant reading tasks on literary data. It allows us to estimate what people talk about. Especially Latent Dirichlet Allocation (LDA) has shown its usefulness, as it is unsupervised, robust, easy to use, scalable, and it offers interpretable results. In a preliminary study, we apply LDA to a corpus of New High German poetry (textgrid, with 51k poems, 8m token), and use the distribution of topics over documents for a classification of poems into time periods and for authorship attribution.

Connected through hyperedge HE-1 (induced by source paper "po emo conceptualization annotation and modeling of aesthetic emotions in german and english poetry"):
  with members:
    - "towards multimodal emotion recognition in german speech events in cars using transfer learning"
    - "dens a dataset for multi class emotion analysis"
    - "semantic change and emerging tropes in a large corpus of new high german poetry"
    - "multi task learning for argumentation mining in low resource settings"
    - "iest wassa 2018 implicit emotions shared task"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "gated feedback recurrent neural networks"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-2 (induced by source paper "semantic change and emerging tropes in a large corpus of new high german poetry"):
  with members:
    - "diachronic word embeddings and semantic shifts a survey"
    - "on the linearity of semantic change investigating meaning variation via dynamic graph models"
    - "diachronic word embeddings reveal statistical laws of semantic change"

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)