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: "incorporating context and external knowledge for pronoun coreference resolution"
  Abstract: Linking pronominal expressions to the correct references requires, in many cases, better analysis of the contextual information and external knowledge. In this paper, we propose a two-layer model for pronoun coreference resolution that leverages both context and external knowledge, where a knowledge attention mechanism is designed to ensure the model leveraging the appropriate source of external knowledge based on different context. Experimental results demonstrate the validity and effectiveness of our model, where it outperforms state-of-the-art models by a large margin.

Connected through hyperedge HE-1 (induced by source paper "multiplex word embeddings for selectional preference acquisition"):
  with members:
    - "knowledge aware pronoun coreference resolution"
    - "sp 10k a large scale evaluation set for selectional preference acquisition"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "measuring thematic fit with distributional feature overlap"
    - "dynamics of core of language vocabulary"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-2 (induced by source paper "a neural entity coreference resolution review"):
  with members:
    - "context in neural machine translation a review of models and evaluations"
    - "gendered ambiguous pronouns shared task boosting model confidence by evidence pooling"
    - "knowledge aware pronoun coreference resolution"
    - "measuring bias in contextualized word representations"
    - "improving generalization in coreference resolution via adversarial training"
    - "look again at the syntax relational graph convolutional network for gendered ambiguous pronoun resolution"
    - "gender bias in contextualized word embeddings"
    - "semantically constrained multilayer annotation the case of coreference"

Connected through hyperedge HE-3 (induced by source paper "what you see is what you get visual pronoun coreference resolution in dialogues"):
  with members:
    - "knowledge aware pronoun coreference resolution"
    - "preco a large scale dataset in preschool vocabulary for coreference resolution"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "deep reinforcement learning for mention ranking coreference models"
    - "generation and comprehension of unambiguous object descriptions"
    - "vqa visual question answering"

Connected through hyperedge HE-4 (induced by source paper "rewarding coreference resolvers for being consistent with world knowledge"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "end to end neural coreference resolution"
    - "distral robust multitask reinforcement learning"
    - "row less universal schema"
    - "deep reinforcement learning for mention ranking coreference models"
    - "multilingual relation extraction using compositional universal schema"

Connected through hyperedge HE-5 (induced by source paper "knowledge aware pronoun coreference resolution"):
  with members:
    - "aser a large scale eventuality knowledge graph"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "the hard core coreference corpus removing gender and number cues for difficult pronominal anaphora resolution"
    - "end to end neural coreference resolution"
    - "deep reinforcement learning for mention ranking coreference models"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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)