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: "words or characters fine grained gating for reading comprehension"
  Abstract: Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension. We present a fine-grained gating mechanism to dynamically combine word-level and character-level representations based on properties of the words. We also extend the idea of fine-grained gating to modeling the interaction between questions and paragraphs for reading comprehension. Experiments show that our approach can improve the performance on reading comprehension tasks, achieving new state-of-the-art results on the Children's Book Test dataset. To demonstrate the generality of our gating mechanism, we also show improved results on a social media tag prediction task.

Connected through hyperedge HE-1 (induced by source paper "gating mechanisms for combining character and word level word representations an empirical study"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "learning universal sentence representations with mean max attention autoencoder"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "discourse marker augmented network with reinforcement learning for natural language inference"):
  with members:
    - "natural language inference over interaction space"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a compare propagate architecture with alignment factorization for natural language inference"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "dissent sentence representation learning from explicit discourse relations"
    - "learned in translation contextualized word vectors"
    - "shortcut stacked sentence encoders for multi domain inference"

Connected through hyperedge HE-4 (induced by source paper "contextual recurrent units for cloze style reading comprehension"):
  with members:
    - "dataset and neural recurrent sequence labeling model for open domain factoid question answering"
    - "reasoning with memory augmented neural networks for language comprehension"
    - "text understanding with the attention sum reader network"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "iterative alternating neural attention for machine reading"
    - "consensus attention based neural networks for chinese reading comprehension"
    - "ac blstm asymmetric convolutional bidirectional lstm networks for text classification"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "effective subword segmentation for text comprehension"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "dual co matching network for multi choice reading comprehension"
    - "i know what you want semantic learning for text comprehension"
    - "graph based bilingual word embedding for statistical machine translation"
    - "open vocabulary learning for neural chinese pinyin ime"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"

Connected through hyperedge HE-6 (induced by source paper "multi task learning for machine reading comprehension"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "a question focused multi factor attention network for question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "the natural language decathlon multitask learning as question answering"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "qanet combining local convolution with global self attention for reading comprehension"

Connected through hyperedge HE-7 (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"

Connected through hyperedge HE-8 (induced by source paper "effective character augmented word embedding for machine reading comprehension"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "lingke a fine grained multi turn chatbot for customer service"
    - "triviaqa a large scale distantly supervised challenge dataset 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)