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: "broad context language modeling as reading comprehension"
  Abstract: Progress in text understanding has been driven by large datasets that test particular capabilities, like recent datasets for reading comprehension (Hermann et al., 2015). We focus here on the LAMBADA dataset (Paperno et al., 2016), a word prediction task requiring broader context than the immediate sentence. We view LAMBADA as a reading comprehension problem and apply comprehension models based on neural networks. Though these models are constrained to choose a word from the context, they improve the state of the art on LAMBADA from 7.3% to 49%. We analyze 100 instances, finding that neural network readers perform well in cases that involve selecting a name from the context based on dialogue or discourse cues but struggle when coreference resolution or external knowledge is needed.

Connected through hyperedge HE-1 (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"
    - "subword augmented embedding for cloze reading comprehension"
    - "on the limitations of unsupervised bilingual dictionary induction"

Connected through hyperedge HE-2 (induced by source paper "neural shuffle exchange networks sequence processing in o n log n time"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "star transformer"
    - "universal transformers"
    - "generating long sequences with sparse transformers"
    - "tensor2tensor for neural machine translation"
    - "recent advances in neural program synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character level language modeling with deeper self attention"

Connected through hyperedge HE-3 (induced by source paper "universal transformers"):
  with members:
    - "hyperbolic attention networks"
    - "weighted transformer network for machine translation"
    - "neural models for reasoning over multiple mentions using coreference"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "a structured self attentive sentence embedding"
    - "linguistic knowledge as memory for recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "attending to entities for better text understanding"):
  with members:
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "spanbert improving pre training by representing and predicting spans"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "universal transformers"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "probing neural network comprehension of natural language arguments"
    - "roberta a robustly optimized bert pretraining approach"
    - "entity tracking improves cloze style reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "a simple method for commonsense reasoning"):
  with members:
    - "solving hard coreference problems"
    - "achieving human parity on automatic chinese to english news translation"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "fine tuned language models for text classification"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"
    - "probabilistic reasoning via deep learning neural association models"

Connected through hyperedge HE-6 (induced by source paper "neural models for reasoning over multiple mentions using coreference"):
  with members:
    - "cross sentence n ary relation extraction with graph lstms"
    - "end to end neural coreference resolution"
    - "syntax aware lstm model for chinese semantic role labeling"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "linguistic knowledge as memory for recurrent neural networks"
    - "dynamic entity representations in neural language models"

Connected through hyperedge HE-7 (induced by source paper "entity tracking improves cloze style reading comprehension"):
  with members:
    - "neural models for reasoning over multiple mentions using coreference"
    - "attention over attention neural networks for reading comprehension"
    - "linguistic knowledge as memory for recurrent neural networks"
    - "text understanding with the attention sum reader network"
    - "the lambada dataset word prediction requiring a broad discourse context"
    - "learning global features for coreference resolution"
    - "tracking the world state with recurrent entity networks"
    - "who did what a large scale person centered cloze dataset"

Connected through hyperedge HE-8 (induced by source paper "linguistic knowledge as memory for recurrent neural networks"):
  with members:
    - "frustratingly short attention spans in neural language modeling"
    - "a neural knowledge language model"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "reasoning with memory augmented neural networks for language comprehension"
    - "gated graph sequence neural networks"
    - "text understanding with the attention sum reader network"
    - "squad 100 000 questions for machine comprehension of text"
    - "the lambada dataset word prediction requiring a broad discourse context"

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)