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: "learning global features for coreference resolution"
  Abstract: There is compelling evidence that coreference prediction would benefit from modeling global information about entity-clusters. Yet, state-of-the-art performance can be achieved with systems treating each mention prediction independently, which we attribute to the inherent difficulty of crafting informative cluster-level features. We instead propose to use recurrent neural networks (RNNs) to learn latent, global representations of entity clusters directly from their mentions. We show that such representations are especially useful for the prediction of pronominal mentions, and can be incorporated into an end-to-end coreference system that outperforms the state of the art without requiring any additional search.

Connected through hyperedge HE-1 (induced by source paper "multi task learning based neural bridging reference resolution"):
  with members:
    - "bam born again multi task networks for natural language understanding"
    - "enhanced word representations for bridging anaphora resolution"
    - "a deterministic algorithm for bridging anaphora resolution"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "scheduled multi task learning from syntax to translation"
    - "end to end neural coreference resolution"

Connected through hyperedge HE-2 (induced by source paper "enteval a holistic evaluation benchmark for entity representations"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "joint learning of named entity recognition and entity linking"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "evaluation benchmarks and learning criteriafor discourse aware sentence representations"
    - "ernie enhanced representation through knowledge integration"
    - "zero shot entity linking by reading entity descriptions"
    - "socialiqa commonsense reasoning about social interactions"
    - "seeing things from a different angle discovering diverse perspectives about claims"

Connected through hyperedge HE-3 (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"
    - "bert for coreference resolution baselines and analysis"
    - "gender bias in contextualized word embeddings"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "a neural entity coreference resolution review"):
  with members:
    - "context in neural machine translation a review of models and evaluations"
    - "incorporating context and external knowledge for pronoun coreference resolution"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "effective use of transformer networks for entity tracking"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "spanbert improving pre training by representing and predicting spans"
    - "attention is not explanation"
    - "tracking discrete and continuous entity state for process understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (induced by source paper "what do entity centric models learn insights from entity linking in multi party dialogue"):
  with members:
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "amore upf at semeval 2018 task 4 bilstm with entity library"
    - "evaluating theory of mind in question answering"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "relnet end to end modeling of entities relations"
    - "improving coreference resolution by learning entity level distributed representations"
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "representation of linguistic form and function in recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog"):
  with members:
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "referring relationships"
    - "end to end neural coreference resolution"
    - "generating descriptions with grounded and co referenced people"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "learning to reason end to end module networks for visual question answering"
    - "improving coreference resolution by learning entity level distributed representations"
    - "guesswhat visual object discovery through multi modal dialogue"

Connected through hyperedge HE-8 (induced by source paper "look again at the syntax relational graph convolutional network for gendered ambiguous pronoun resolution"):
  with members:
    - "neural coreference resolution with deep biaffine attention by joint mention detection and mention clustering"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "anaphora and coreference resolution a review"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural coreference resolution"
    - "attention is all you need"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "gated graph sequence neural networks"

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)