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: "multi perspective context matching for machine comprehension"
  Abstract: Previous machine comprehension (MC) datasets are either too small to train end-to-end deep learning models, or not difficult enough to evaluate the ability of current MC techniques. The newly released SQuAD dataset alleviates these limitations, and gives us a chance to develop more realistic MC models. Based on this dataset, we propose a Multi-Perspective Context Matching (MPCM) model, which is an end-to-end system that directly predicts the answer beginning and ending points in a passage. Our model first adjusts each word-embedding vector in the passage by multiplying a relevancy weight computed against the question. Then, we encode the question and weighted passage by using bi-directional LSTMs. For each point in the passage, our model matches the context of this point against the encoded question from multiple perspectives and produces a matching vector. Given those matched vectors, we employ another bi-directional LSTM to aggregate all the information and predict the beginning and ending points. Experimental result on the test set of SQuAD shows that our model achieves a competitive result on the leaderboard.

Connected through hyperedge HE-1 (induced by source paper "description based text classification with reinforcement learning"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "docbert bert for document classification"
    - "question answering is a format when is it useful"
    - "entity relation extraction as multi turn question answering"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "coreference resolution as query based span prediction"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"

Connected through hyperedge HE-2 (induced by source paper "a pilot study on multiple choice machine reading comprehension for vietnamese texts"):
  with members:
    - "vietnamese transition based dependency parsing with supertag features"
    - "semeval 2017 task 3 community question answering"
    - "korquad1 0 korean qa dataset for machine reading comprehension"
    - "error analysis for vietnamese named entity recognition on deep neural network models"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "sberquad russian reading comprehension dataset description and analysis"
    - "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
    - "lstm easy first dependency parsing with pre trained word embeddings and character level word embeddings in vietnamese"

Connected through hyperedge HE-3 (induced by source paper "dice loss for data imbalanced nlp tasks"):
  with members:
    - "a unified mrc framework for named entity recognition"
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "libra r cnn towards balanced learning for object detection"
    - "glyce glyph vectors for chinese character representations"
    - "towards accurate one stage object detection with ap loss"
    - "dsreg using distant supervision as a regularizer"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "chinese ner using lattice lstm"

Connected through hyperedge HE-4 (induced by source paper "macnet transferring knowledge from machine comprehension to sequence to sequence models"):
  with members:
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "selective encoding for abstractive sentence summarization"
    - "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"

Connected through hyperedge HE-5 (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-6 (induced by source paper "a unified mrc framework for named entity recognition"):
  with members:
    - "entity relation extraction as multi turn question answering"
    - "glyce glyph vectors for chinese character representations"
    - "nested named entity recognition via second best sequence learning and decoding"
    - "sequence to nuggets nested entity mention detection via anchor region networks"
    - "merge and label a novel neural network architecture for nested ner"
    - "neural architectures for nested ner through linearization"
    - "a general framework for information extraction using dynamic span graphs"
    - "chinese ner using lattice lstm"

Connected through hyperedge HE-7 (induced by source paper "entity relation extraction as multi turn question answering"):
  with members:
    - "the natural language decathlon multitask learning as question answering"
    - "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"
    - "adversarial training for multi context joint entity and relation extraction"
    - "a hierarchical framework for relation extraction with reinforcement learning"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-8 (induced by source paper "query based named entity recognition"):
  with members:
    - "glyce glyph vectors for chinese character representations"
    - "chinese ner using lattice lstm"
    - "the natural language decathlon multitask learning as question answering"
    - "neural segmental hypergraphs for overlapping mention recognition"
    - "deep contextualized word representations"
    - "a neural transition based model for nested mention recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "zero shot relation extraction via 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)