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: "bert for coreference resolution baselines and analysis"
  Abstract: We apply BERT to coreference resolution, achieving strong improvements on the OntoNotes (+3.9 F1) and GAP (+11.5 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but distinct entities (e.g., President and CEO). However, there is still room for improvement in modeling document-level context, conversations, and mention paraphrasing. Our code and models are publicly available.

Connected through hyperedge HE-1 (induced by source paper "coreference resolution as query based span prediction"):
  with members:
    - "a unified mrc framework for named entity recognition"
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "ellipsis and coreference resolution as question answering"
    - "spanbert improving pre training by representing and predicting spans"
    - "entity relation extraction as multi turn question answering"
    - "gender bias in contextualized word embeddings"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"

Connected through hyperedge HE-2 (induced by source paper "blockwise self attention for long document understanding"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "latent retrieval for weakly supervised open domain question answering"
    - "adaptively sparse transformers"
    - "passage re ranking with bert"
    - "spanbert improving pre training by representing and predicting spans"
    - "matching the blanks distributional similarity for relation learning"
    - "low memory neural network training a technical report"
    - "transformer xl attentive language models beyond a fixed length context"

Connected through hyperedge HE-3 (induced by source paper "aloha artificial learning of human attributes for dialogue agents"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "meetup a corpus of joint activity dialogues in a visual environment"
    - "learning to speak and act in a fantasy text adventure game"
    - "keep calm and switch on preserving sentiment and fluency in semantic text exchange"
    - "game based video context dialogue"
    - "alquist the alexa prize socialbot"

Connected through hyperedge HE-4 (induced by source paper "a cluster ranking model for full anaphora resolution"):
  with members:
    - "preco a large scale dataset in preschool vocabulary for coreference resolution"
    - "neural coreference resolution with deep biaffine attention by joint mention detection and mention clustering"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural coreference resolution"
    - "improving coreference resolution by learning entity level distributed representations"
    - "learning global features for coreference resolution"

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)