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: "ruminating reader reasoning with gated multi hop attention"
  Abstract: To answer the question in machine comprehension (MC) task, the models need to establish the interaction between the question and the context. To tackle the problem that the single-pass model cannot reflect on and correct its answer, we present Ruminating Reader. Ruminating Reader adds a second pass of attention and a novel information fusion component to the Bi-Directional Attention Flow model (BiDAF). We propose novel layer structures that construct an query-aware context vector representation and fuse encoding representation with intermediate representation on top of BiDAF model. We show that a multi-hop attention mechanism can be applied to a bi-directional attention structure. In experiments on SQuAD, we find that the Reader outperforms the BiDAF baseline by a substantial margin, and matches or surpasses the performance of all other published systems.

Connected through hyperedge HE-1 (induced by source paper "gated mechanism for attention based multimodal sentiment analysis"):
  with members:
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "multi attention recurrent network for human communication comprehension"
    - "memory fusion network for multi view sequential learning"
    - "tensor fusion network for multimodal sentiment analysis"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "recent advances in neural question generation"):
  with members:
    - "question generation from sql queries improves neural semantic parsing"
    - "towards a better metric for evaluating question generation systems"
    - "improving neural question generation using answer separation"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "automating reading comprehension by generating question and answer pairs"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "neural generation of diverse questions using answer focus contextual and linguistic features"

Connected through hyperedge HE-3 (induced by source paper "making neural machine reading comprehension faster"):
  with members:
    - "neural machine translation for bilingually scarce scenarios a deep multi task learning approach"
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "a fully attention based information retriever"
    - "lightweight convolutional approaches to reading comprehension on squad"
    - "phase conductor on multi layered attentions for machine comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "learning to compute word embeddings on the fly"
    - "contextualized word representations for reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "generating questions for knowledge bases via incorporating diversified contexts and answer aware loss"):
  with members:
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "simplequestions nearly solved a new upperbound and baseline approach"
    - "attention is all you need"
    - "strong baselines for simple question answering over knowledge graphs with and without neural networks"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-5 (induced by source paper "let me know what to ask interrogative word aware question generation"):
  with members:
    - "unsupervised question answering by cloze translation"
    - "learning to generate questions by learningwhat not to generate"
    - "recent advances in neural question generation"
    - "improving neural question generation using answer separation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural models for key phrase detection and question generation"
    - "learning to ask neural question generation for reading comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-6 (induced by source paper "frustratingly poor performance of reading comprehension models on non adversarial examples"):
  with members:
    - "deep text classification can be fooled"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention over attention neural networks for reading comprehension"
    - "newsqa a machine comprehension dataset"
    - "squad 100 000 questions for machine comprehension of text"
    - "ms marco a human generated machine reading comprehension dataset"
    - "dynamic coattention networks for question answering"
    - "understanding neural networks through representation erasure"

Connected through hyperedge HE-7 (induced by source paper "graph message passing with cross location attentions for long term ili prediction"):
  with members:
    - "attention is all you need"
    - "long short term memory networks for machine reading"
    - "semi supervised classification with graph convolutional networks"
    - "end to end memory networks"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "a fully attention based information retriever"):
  with members:
    - "phase conductor on multi layered attentions for machine comprehension"
    - "learning to compute word embeddings on the fly"
    - "contextualized word representations for reading comprehension"
    - "reinforced mnemonic reader for machine comprehension"
    - "smarnet teaching machines to read and comprehend like human"
    - "attention is all you need"
    - "structural embedding of syntactic trees for machine comprehension"
    - "stochastic answer networks for machine 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)