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: "dynamic coattention networks for question answering"
  Abstract: Several deep learning models have been proposed for question answering. However, due to their single-pass nature, they have no way to recover from local maxima corresponding to incorrect answers. To address this problem, we introduce the Dynamic Coattention Network (DCN) for question answering. The DCN first fuses co-dependent representations of the question and the document in order to focus on relevant parts of both. Then a dynamic pointing decoder iterates over potential answer spans. This iterative procedure enables the model to recover from initial local maxima corresponding to incorrect answers. On the Stanford question answering dataset, a single DCN model improves the previous state of the art from 71.0% F1 to 75.9%, while a DCN ensemble obtains 80.4% F1.

Connected through hyperedge HE-1 (induced by source paper "description based text classification with reinforcement learning"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "docbert bert for document classification"
    - "question answering is a format when is it useful"
    - "entity relation extraction as multi turn question answering"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "coreference resolution as query based span prediction"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"

Connected through hyperedge HE-2 (induced by source paper "a study of the tasks and models in machine reading comprehension"):
  with members:
    - "a deep cascade model for multi document reading comprehension"
    - "multi style generative reading comprehension"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "attention guided answer distillation for machine reading comprehension"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-3 (induced by source paper "see more know more unsupervised video object segmentation with co attention siamese networks"):
  with members:
    - "deep visual attention prediction"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "videomatch matching based video object segmentation"
    - "pairwise body part attention for recognizing human object interactions"

Connected through hyperedge HE-4 (induced by source paper "dc bert decoupling question and document for efficient contextual encoding"):
  with members:
    - "q8bert quantized 8bit bert"
    - "latent retrieval for weakly supervised open domain question answering"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "bert rediscovers the classical nlp pipeline"
    - "retrieve read rerank towards end to end multi document reading comprehension"
    - "learning to retrieve reasoning paths over wikipedia graph for question answering"
    - "a discrete hard em approach for weakly supervised question answering"
    - "self assembling modular networks for interpretable multi hop reasoning"

Connected through hyperedge HE-5 (induced by source paper "inf vae a variational autoencoder framework to integrate homophily and influence in diffusion prediction"):
  with members:
    - "an induced multi relational framework for answer selection in community question answer platforms"
    - "deepinf social influence prediction with deep learning"
    - "dynamic graph representation learning via self attention networks"
    - "attention is all you need"
    - "representation learning on graphs methods and applications"
    - "motif based convolutional neural network on graphs"
    - "topological recurrent neural network for diffusion prediction"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-6 (induced by source paper "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "personalized dialogue generation with diversified traits"
    - "learning to write with cooperative discriminators"
    - "training millions of personalized dialogue agents"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "polite dialogue generation without parallel data"

Connected through hyperedge HE-7 (induced by source paper "multi style generative reading comprehension"):
  with members:
    - "learning and evaluating general linguistic intelligence"
    - "a deep cascade model for multi document reading comprehension"
    - "densely connected attention propagation for reading comprehension"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "the natural language decathlon multitask learning as question answering"
    - "bottom up abstractive summarization"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-8 (induced by source paper "machine reading comprehension a literature review"):
  with members:
    - "towards inference oriented reading comprehension parallelqa"
    - "generating wikipedia by summarizing long sequences"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "know what you don t know unanswerable questions for squad"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "qanet combining local convolution with global self attention for reading comprehension"

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)