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 importance of encoding logic rules in sentiment classification"
  Abstract: We analyze the performance of different sentiment classification models on syntactically complex inputs like A-but-B sentences. The first contribution of this analysis addresses reproducible research: to meaningfully compare different models, their accuracies must be averaged over far more random seeds than what has traditionally been reported. With proper averaging in place, we notice that the distillation model described in arXiv:1603.06318v4 [cs.LG], which incorporates explicit logic rules for sentiment classification, is ineffective. In contrast, using contextualized ELMo embeddings (arXiv:1802.05365v2 [cs.CL]) instead of logic rules yields significantly better performance. Additionally, we provide analysis and visualizations that demonstrate ELMo's ability to implicitly learn logic rules. Finally, a crowdsourced analysis reveals how ELMo outperforms baseline models even on sentences with ambiguous sentiment labels.

Connected through hyperedge HE-1 (induced by source paper "cross lingual data transformation and combination for text classification"):
  with members:
    - "learning word vectors for 157 languages"
    - "an empirical evaluation of deep learning for icd 9 code assignment using mimic iii clinical notes"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "social emotion mining techniques for facebook posts reaction prediction"
    - "fasttext zip compressing text classification models"
    - "one model to learn them all"
    - "convolutional neural networks for medical diagnosis from admission notes"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-2 (induced by source paper "uncovering relations for marketing knowledge representation"):
  with members:
    - "learning dual retrieval module for semi supervised relation extraction"
    - "logic rules powered knowledge graph embedding"
    - "connecting language and knowledge with heterogeneous representations for neural relation extraction"
    - "reinforcement learning for relation classification from noisy data"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "harnessing deep neural networks with logic rules"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "end to end relation extraction using lstms on sequences and tree structures"

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)