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: "deep reinforcement learning for chinese zero pronoun resolution"
  Abstract: Deep neural network models for Chinese zero pronoun resolution learn semantic information for zero pronoun and candidate antecedents, but tend to be short-sighted---they often make local decisions. They typically predict coreference chains between the zero pronoun and one single candidate antecedent one link at a time, while overlooking their long-term influence on future decisions. Ideally, modeling useful information of preceding potential antecedents is critical when later predicting zero pronoun-candidate antecedent pairs. In this study, we show how to integrate local and global decision-making by exploiting deep reinforcement learning models. With the help of the reinforcement learning agent, our model learns the policy of selecting antecedents in a sequential manner, where useful information provided by earlier predicted antecedents could be utilized for making later coreference decisions. Experimental results on OntoNotes 5.0 dataset show that our technique surpasses the state-of-the-art models.

Connected through hyperedge HE-1 (induced by source paper "generating persona consistent dialogues by exploiting natural language inference"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "exploiting persona information for diverse generation of conversational responses"
    - "evaluating coherence in dialogue systems using entailment"
    - "a study of reinforcement learning for neural machine translation"
    - "dialogue natural language inference"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semantic sentence matching with densely connected recurrent and co attentive information"

Connected through hyperedge HE-2 (induced by source paper "an empirical comparison on imitation learning and reinforcement learning for paraphrase generation"):
  with members:
    - "deep reinforcement learning for sequence to sequence models"
    - "learning semantic sentence embeddings using pair wise discriminator"
    - "the natural language decathlon multitask learning as question answering"
    - "a study of reinforcement learning for neural machine translation"
    - "learning semantic sentence embeddings using sequential pair wise discriminator"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "a continuously growing dataset of sentential paraphrases"
    - "learning to paraphrase for question answering"

Connected through hyperedge HE-3 (induced by source paper "where s my head definition dataset and models for numeric fused heads identification and resolution"):
  with members:
    - "neural coreference resolution with deep biaffine attention by joint mention detection and mention clustering"
    - "numeracy for language models evaluating and improving their ability to predict numbers"
    - "allennlp a deep semantic natural language processing platform"
    - "neural arithmetic logic units"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "anaphora and coreference resolution a review"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "recovering dropped pronouns in chinese conversations via modeling their referents"):
  with members:
    - "analogical reasoning on chinese morphological and semantic relations"
    - "translating pro drop languages with reconstruction models"
    - "structured attention networks"
    - "hierarchical recurrent attention network for response generation"
    - "a novel approach to dropped pronoun translation"
    - "tracking the world state with recurrent entity networks"
    - "key value memory networks for directly reading documents"
    - "end to end memory networks"

Connected through hyperedge HE-5 (induced by source paper "joint entity linking with deep reinforcement learning"):
  with members:
    - "neural collective entity linking"
    - "improving entity linking by modeling latent relations between mentions"
    - "pair linking for collective entity disambiguation two could be better than all"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "xgboost a scalable tree boosting system"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "leveraging deep neural networks and knowledge graphs for entity disambiguation"

Connected through hyperedge HE-6 (induced by source paper "a stack propagation framework with token level intent detection for spoken language understanding"):
  with members:
    - "bert for joint intent classification and slot filling"
    - "joint slot filling and intent detection via capsule neural networks"
    - "a bi model based rnn semantic frame parsing model for intent detection and slot filling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "stochastic multiple choice learning for training diverse deep ensembles"

Connected through hyperedge HE-7 (induced by source paper "towards explainable nlp a generative explanation framework for text classification"):
  with members:
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "deep contextualized word representations"
    - "e snli natural language inference with natural language explanations"
    - "improving explainable recommendations with synthetic reviews"
    - "mojitalk generating emotional responses at scale"
    - "attention is all you need"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-8 (induced by source paper "a deep reinforced sequence to set model for multi label text classification"):
  with members:
    - "sememe prediction learning semantic knowledge from unstructured textual wiki descriptions"
    - "a deep reinforced model for abstractive summarization"
    - "deep reinforcement learning for dialogue generation"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "self critical sequence training for image captioning"
    - "sequence level training with recurrent neural networks"
    - "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)