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: "smarnet teaching machines to read and comprehend like human"
  Abstract: Machine Comprehension (MC) is a challenging task in Natural Language Processing field, which aims to guide the machine to comprehend a passage and answer the given question. Many existing approaches on MC task are suffering the inefficiency in some bottlenecks, such as insufficient lexical understanding, complex question-passage interaction, incorrect answer extraction and so on. In this paper, we address these problems from the viewpoint of how humans deal with reading tests in a scientific way. Specifically, we first propose a novel lexical gating mechanism to dynamically combine the words and characters representations. We then guide the machines to read in an interactive way with attention mechanism and memory network. Finally we add a checking layer to refine the answer for insurance. The extensive experiments on two popular datasets SQuAD and TriviaQA show that our method exceeds considerable performance than most state-of-the-art solutions at the time of submission.

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"
    - "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-2 (induced by source paper "integrated triaging for fast reading comprehension"):
  with members:
    - "fastfusionnet new state of the art for dawnbench squad"
    - "phrase indexed question answering a new challenge for scalable document comprehension"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "bottom up abstractive summarization"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "resource aware person re identification across multiple resolutions"

Connected through hyperedge HE-3 (induced by source paper "a deep cascade model for multi document reading comprehension"):
  with members:
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "reinforced mnemonic reader for machine comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "r 3 reinforced reader ranker for open domain question answering"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "dialogue act recognition via crf attentive structured network"):
  with members:
    - "structured attention networks"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "capturing long range contextual dependencies with memory enhanced conditional random fields"
    - "text understanding with the attention sum reader network"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "a latent variable recurrent neural network for discourse relation language models"

Connected through hyperedge HE-6 (induced by source paper "multi mention learning for reading comprehension with neural cascades"):
  with members:
    - "reinforced mnemonic reader for machine comprehension"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "learning recurrent span representations for extractive question answering"
    - "reading wikipedia to answer open domain questions"
    - "a decomposable attention model for natural language inference"
    - "bidirectional attention flow for machine comprehension"
    - "text understanding with the attention sum reader network"

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)