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: "from pos tagging to dependency parsing for biomedical event extraction"
  Abstract: Background#R##N#Given the importance of relation or event extraction from biomedical research publications to support knowledge capture and synthesis, and the strong dependency of approaches to this information extraction task on syntactic information, it is valuable to understand which approaches to syntactic processing of biomedical text have the highest performance.

Connected through hyperedge HE-1 (induced by source paper "scibert a pretrained language model for scientific text"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "collabonet collaboration of deep neural networks for biomedical named entity recognition"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "publicly available clinical bert embeddings"
    - "scispacy fast and robust models for biomedical natural language processing"
    - "a corpus with multi level annotations of patients interventions and outcomes to support language processing for medical literature"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"

Connected through hyperedge HE-2 (induced by source paper "scispacy fast and robust models for biomedical natural language processing"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "extending a parser to distant domains using a few dozen partially annotated examples"
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "construction of the literature graph in semantic scholar"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "effective use of bidirectional language modeling for transfer learning in biomedical named entity recognition"
    - "deep biaffine attention for neural dependency parsing"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

Connected through hyperedge HE-3 (induced by source paper "an improved neural network model for joint"):
  with members:
    - "neural probabilistic model for non projective mst parsing"
    - "a novel neural network model for joint pos tagging and graph based dependency parsing"
    - "globally normalized transition based neural networks"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "dependency parsing as head selection"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "distilling an ensemble of greedy dependency parsers into one mst parser"

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)