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: "phase conductor on multi layered attentions for machine comprehension"
  Abstract: Attention models have been intensively studied to improve NLP tasks such as machine comprehension via both question-aware passage attention model and self-matching attention model. Our research proposes phase conductor (PhaseCond) for attention models in two meaningful ways. First, PhaseCond, an architecture of multi-layered attention models, consists of multiple phases each implementing a stack of attention layers producing passage representations and a stack of inner or outer fusion layers regulating the information flow. Second, we extend and improve the dot-product attention function for PhaseCond by simultaneously encoding multiple question and passage embedding layers from different perspectives. We demonstrate the effectiveness of our proposed model PhaseCond on the SQuAD dataset, showing that our model significantly outperforms both state-of-the-art single-layered and multiple-layered attention models. We deepen our results with new findings via both detailed qualitative analysis and visualized examples showing the dynamic changes through multi-layered attention models.

Connected through hyperedge HE-1 (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"
    - "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"
    - "smarnet teaching machines to read and comprehend like human"

Connected through hyperedge HE-2 (induced by source paper "pullnet open domain question answering with iterative retrieval on knowledge bases and text"):
  with members:
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "the web as a knowledge base for answering complex questions"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "variational reasoning for question answering with knowledge graph"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"

Connected through hyperedge HE-3 (induced by source paper "a fully attention based information retriever"):
  with members:
    - "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"
    - "end to end answer chunk extraction and ranking for reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"):
  with members:
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "reinforced mnemonic reader for machine comprehension"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "focal loss for dense object detection"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention over attention neural networks for reading comprehension"
    - "stochastic answer networks for machine reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "weaver deep co encoding of questions and documents for machine reading"):
  with members:
    - "reinforced mnemonic reader for machine comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "r 3 reinforced reader ranker for open domain question answering"
    - "stochastic answer networks for machine reading comprehension"
    - "parlai a dialog research software platform"
    - "advances in pre training distributed word representations"
    - "fusionnet fusing via fully aware attention with application to machine comprehension"

Connected through hyperedge HE-6 (induced by source paper "question aware sentence gating networks for question and answering"):
  with members:
    - "reinforced mnemonic reader for machine comprehension"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "fusionnet fusing via fully aware attention with application to machine comprehension"
    - "recurrent neural network based sentence encoder with gated attention for natural language inference"
    - "learning to compose task specific tree structures"
    - "robust multilingual named entity recognition with shallow semi supervised features"
    - "text understanding with the attention sum reader network"

Connected through hyperedge HE-7 (induced by source paper "stochastic answer networks for machine reading comprehension"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "stochastic answer networks for natural language inference"
    - "reinforced mnemonic reader for machine comprehension"
    - "an empirical analysis of multiple turn reasoning strategies in reading comprehension tasks"
    - "attention is all you need"
    - "learned in translation contextualized word vectors"
    - "learning recurrent span representations for extractive question answering"
    - "making neural qa as simple as possible but not simpler"

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)