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: "lexical features in coreference resolution to be used with caution"
  Abstract: Lexical features are a major source of information in state-of-the-art coreference resolvers. Lexical features implicitly model some of the linguistic phenomena at a fine granularity level. They are especially useful for representing the context of mentions. In this paper we investigate a drawback of using many lexical features in state-of-the-art coreference resolvers. We show that if coreference resolvers mainly rely on lexical features, they can hardly generalize to unseen domains. Furthermore, we show that the current coreference resolution evaluation is clearly flawed by only evaluating on a specific split of a specific dataset in which there is a notable overlap between the training, development and test sets.

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"
    - "using linguistic features to improve the generalization capability of neural coreference resolvers"
    - "transfer learning for named entity recognition with neural networks"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-2 (induced by source paper "can a gorilla ride a camel learning semantic plausibility from text"):
  with members:
    - "universal dependency parsing from scratch"
    - "solving hard coreference problems"
    - "sp 10k a large scale evaluation set for selectional preference acquisition"
    - "exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge"
    - "do neural language representations learn physical commonsense"
    - "probing neural network comprehension of natural language arguments"
    - "winogrande an adversarial winograd schema challenge at scale"
    - "annotation artifacts in natural language inference data"

Connected through hyperedge HE-3 (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"
    - "using linguistic features to improve the generalization capability of neural coreference resolvers"
    - "question answering via integer programming over semi structured knowledge"

Connected through hyperedge HE-4 (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"
    - "using linguistic features to improve the generalization capability of neural coreference resolvers"
    - "improving coreference resolution by learning entity level distributed representations"
    - "deep reinforcement learning for mention ranking coreference models"

Connected through hyperedge HE-5 (induced by source paper "anaphora and coreference resolution a review"):
  with members:
    - "recent trends in deep learning based personality detection"
    - "solving hard coreference problems"
    - "sentiment and sarcasm classification with multitask learning"
    - "end to end neural coreference resolution"
    - "a mention ranking model for abstract anaphora resolution"
    - "evaluating discourse phenomena in neural machine translation"
    - "recent trends in deep learning based natural language processing"
    - "augmenting end to end dialog systems with commonsense knowledge"

Connected through hyperedge HE-6 (induced by source paper "preco a large scale dataset in preschool vocabulary for coreference resolution"):
  with members:
    - "deep contextualized word representations"
    - "end to end neural coreference resolution"
    - "race large scale reading comprehension dataset from examinations"
    - "improving coreference resolution by learning entity level distributed representations"
    - "squad 100 000 questions for machine comprehension of text"
    - "learning global features for coreference resolution"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "deep reinforcement learning for mention ranking coreference models"

Connected through hyperedge HE-7 (induced by source paper "neural coreference resolution with deep biaffine attention by joint mention detection and mention clustering"):
  with members:
    - "end to end neural coreference resolution"
    - "improving coreference resolution by learning entity level distributed representations"
    - "learning global features for coreference resolution"
    - "deep reinforcement learning for mention ranking coreference models"
    - "adam a method for stochastic optimization"
    - "a large scale exploration of effective global features for a joint entity detection and tracking model"

Connected through hyperedge HE-8 (induced by source paper "using linguistic features to improve the generalization capability of neural coreference resolvers"):
  with members:
    - "end to end neural coreference resolution"
    - "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)