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: "narrowing the modeling gap a cluster ranking approach to coreference resolution"
  Abstract: Traditional learning-based coreference resolvers operate by training the mention-pair model for determining whether two mentions are coreferent or not. Though conceptually simple and easy to understand, the mention-pair model is linguistically rather unappealing and lags far behind the heuristic-based coreference models proposed in the prestatistical NLP era in terms of sophistication. Two independent lines of recent research have attempted to improve the mention-pair model, one by acquiring the mention-ranking model to rank preceding mentions for a given anaphor, and the other by training the entity-mention model to determine whether a preceding cluster is coreferent with a given mention. We propose a cluster-ranking approach to coreference resolution, which combines the strengths of the mention-ranking model and the entity-mention model, and is therefore theoretically more appealing than both of these models. In addition, we seek to improve cluster rankers via two extensions: (1) lexicalization and (2) incorporating knowledge of anaphoricity by jointly modeling anaphoricity determination and coreference resolution. Experimental results on the ACE data sets demonstrate the superior performance of cluster rankers to competing approaches as well as the effectiveness of our two extensions.

Connected through hyperedge HE-1 (induced by source paper "a cluster ranking model for full anaphora resolution"):
  with members:
    - "bert for coreference resolution baselines and analysis"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "improving coreference resolution by learning entity level distributed representations"):
  with members:
    - "learning global features for coreference resolution"
    - "learning to search for dependencies"
    - "learning to search better than your teacher"
    - "efficient programmable learning to search"
    - "polyglot distributed word representations for multilingual nlp"
    - "distributed representations of words and phrases and their compositionality"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "search based structured prediction"

Connected through hyperedge HE-3 (induced by source paper "learning global features for coreference resolution"):
  with members:
    - "visualizing and understanding neural models in nlp"
    - "rnn recurrent library for torch"
    - "transition based dependency parsing with stack long short term memory"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "recurrent neural network regularization"
    - "sequence to sequence learning with neural networks"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "search based structured prediction"

Connected through hyperedge HE-4 (induced by source paper "a split merge framework for comparing clusterings"):
  with members:
    - "objective criteria for the evaluation of clustering methods"

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)