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: "subword augmented embedding for cloze reading comprehension"
  Abstract: Representation learning is the foundation of machine reading comprehension. In state-of-the-art models, deep learning methods broadly use word and character level representations. However, character is not naturally the minimal linguistic unit. In addition, with a simple concatenation of character and word embedding, previous models actually give suboptimal solution. In this paper, we propose to use subword rather than character for word embedding enhancement. We also empirically explore different augmentation strategies on subword-augmented embedding to enhance the cloze-style reading comprehension model reader. In detail, we present a reader that uses subword-level representation to augment word embedding with a short list to handle rare words effectively. A thorough examination is conducted to evaluate the comprehensive performance and generalization ability of the proposed reader. Experimental results show that the proposed approach helps the reader significantly outperform the state-of-the-art baselines on various public datasets.

Connected through hyperedge HE-1 (induced by source paper "sg net syntax guided machine reading comprehension"):
  with members:
    - "semantics aware bert for language understanding"
    - "limit bert linguistic informed multi task bert"
    - "syntax aware neural semantic role labeling with supertags"
    - "global greedy dependency parsing"
    - "option comparison network for multiple choice reading comprehension"
    - "probing contextualized sentence representations with visual awareness"
    - "what does bert look at an analysis of bert s attention"
    - "dual co matching network for multi choice reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "improving pre trained multilingual models with vocabulary expansion"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "gromov wasserstein alignment of word embedding spaces"
    - "generating wikipedia by summarizing long sequences"
    - "bilingual expert can find translation errors"
    - "universal neural machine translation for extremely low resource languages"
    - "deep contextualized word representations"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "on the importance of subword information for morphological tasks in truly low resource languages"):
  with members:
    - "morphological word embeddings"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "a systematic study of leveraging subword information for learning word representations"
    - "modeling language variation and universals a survey on typological linguistics for natural language processing"
    - "generalizing word embeddings using bag of subwords"
    - "deep contextualized word representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "adapting word embeddings to new languages with morphological and phonological subword representations"

Connected through hyperedge HE-4 (induced by source paper "head driven phrase structure grammar parsing on penn treebank"):
  with members:
    - "lattice based transformer encoder for neural machine translation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what s going on in neural constituency parsers an analysis"
    - "two local models for neural constituent parsing"
    - "exploring recombination for efficient decoding of neural machine translation"
    - "multilingual constituency parsing with self attention and pre training"
    - "deep contextualized word representations"
    - "one shot learning for question answering in gaokao history challenge"

Connected through hyperedge HE-5 (induced by source paper "dual co matching network for multi choice reading comprehension"):
  with members:
    - "semantics aware bert for language understanding"
    - "hierarchical contextualized representation for named entity recognition"
    - "convolutional spatial attention model for reading comprehension with multiple choice questions"
    - "option comparison network for multiple choice reading comprehension"
    - "sg net syntax guided machine reading comprehension"
    - "evidence sentence extraction for machine reading comprehension"
    - "lattice based transformer encoder for neural machine translation"
    - "head driven phrase structure grammar parsing on penn treebank"

Connected through hyperedge HE-6 (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"
    - "one shot learning for question answering in gaokao history challenge"

Connected through hyperedge HE-7 (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"
    - "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"
    - "attention over attention neural networks for reading comprehension"

Connected through hyperedge HE-8 (induced by source paper "i know what you want semantic learning for text comprehension"):
  with members:
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "know what you don t know unanswerable questions for squad"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "one shot learning for question answering in gaokao history challenge"
    - "large scale qa srl parsing"
    - "multi passage machine reading comprehension with cross passage answer verification"

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)