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: "using linguistic features to improve the generalization capability of neural coreference resolvers"
  Abstract: Coreference resolution is an intermediate step for text understanding. It is used in tasks and domains for which we do not necessarily have coreference annotated corpora. Therefore, generalization is of special importance for coreference resolution. However, while recent coreference resolvers have notable improvements on the CoNLL dataset, they struggle to generalize properly to new domains or datasets. In this paper, we investigate the role of linguistic features in building more generalizable coreference resolvers. We show that generalization improves only slightly by merely using a set of additional linguistic features. However, employing features and subsets of their values that are informative for coreference resolution, considerably improves generalization. Thanks to better generalization, our system achieves state-of-the-art results in out-of-domain evaluations, e.g., on WikiCoref, our system, which is trained on CoNLL, achieves on-par performance with a system designed for this dataset.

Connected through hyperedge HE-1 (induced by source paper "contextualized embeddings in named entity recognition an empirical study on generalization"):
  with members:
    - "how multilingual is multilingual bert"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"
    - "lexical features in coreference resolution to be used with caution"
    - "transfer learning for named entity recognition with neural networks"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-2 (induced by source paper "improving generalization in coreference resolution via adversarial training"):
  with members:
    - "generating natural language adversarial examples"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "named person coreference in english news"
    - "adversarial training for multi context joint entity and relation extraction"
    - "end to end neural coreference resolution"
    - "lexical features in coreference resolution to be used with caution"
    - "question answering via integer programming over semi structured knowledge"

Connected through hyperedge HE-3 (induced by source paper "using automatically extracted minimum spans to disentangle coreference evaluation from boundary detection"):
  with members:
    - "higher order coreference resolution with coarse to fine inference"
    - "end to end neural coreference resolution"
    - "lexical features in coreference resolution to be used with caution"
    - "improving coreference resolution by learning entity level distributed representations"
    - "deep reinforcement learning for mention ranking coreference models"

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)