human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "linguistically informed relation extraction and neural architectures for nested named entity recognition in bionlp ost 2019"):
  Abstract: Named Entity Recognition (NER) and Relation Extraction (RE) are essential tools in distilling knowledge from biomedical literature. This paper presents our findings from participating in BioNLP Shared Tasks 2019. We addressed Named Entity Recognition including nested entities extraction, Entity Normalization and Relation Extraction. Our proposed approach of Named Entities can be generalized to different languages and we have shown it's effectiveness for English and Spanish text. We investigated linguistic features, hybrid loss including ranking and Conditional Random Fields (CRF), multi-task objective and token-level ensembling strategy to improve NER. We employed dictionary based fuzzy and semantic search to perform Entity Normalization. Finally, our RE system employed Support Vector Machine (SVM) with linguistic features. #R##N#Our NER submission (team:MIC-CIS) ranked first in BB-2019 norm+NER task with standard error rate (SER) of 0.7159 and showed competitive performance on PharmaCo NER task with F1-score of 0.8662. Our RE system ranked first in the SeeDev-binary Relation Extraction Task with F1-score of 0.3738.

Includes members:
  Member "neural relation extraction within and across sentence boundaries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to infer entities properties and their relations from clinical conversations")
  Member "lisa explaining recurrent neural network judgments via layer wise semantic accumulation and example to pattern transformation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop")
  Member "joint bootstrapping machines for high confidence relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nero a neural rule grounding framework for label efficient relation extraction")
  Member "cross sentence n ary relation extraction with graph lstms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive fractional order graph neural network")
  Member "semi supervised multitask learning for sequence labeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exploring self supervised regularization for supervised and semi supervised learning")
  Member "a multi task approach for named entity recognition in social media data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "why attention analyze bilstm deficiency and its remedies in the case of ner")
  Member "enriching word vectors with subword information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "neural architectures for named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "knowledge graphs")

Question: Please tell me which class the source paper 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)