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: "a comparative study of word embeddings for reading comprehension"
  Abstract: The focus of past machine learning research for Reading Comprehension tasks has been primarily on the design of novel deep learning architectures. Here we show that seemingly minor choices made on (1) the use of pre-trained word embeddings, and (2) the representation of out-of-vocabulary tokens at test time, can turn out to have a larger impact than architectural choices on the final performance. We systematically explore several options for these choices, and provide recommendations to researchers working in this area.

Connected through hyperedge HE-1 (induced by source paper "exploring benefits of transfer learning in neural machine translation"):
  with members:
    - "widening the representation bottleneck in neural machine translation with lexical shortcuts"
    - "revisiting low resource neural machine translation a case study"
    - "tackling climate change with machine learning"
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "investigating backtranslation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "enhancing semantic word representations by embedding deeper word relationships"):
  with members:
    - "an ensemble method to produce high quality word embeddings"
    - "text understanding from scratch"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "named entity recognition with bidirectional lstm cnns"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "neural machine translation by jointly learning to align and translate"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"

Connected through hyperedge HE-3 (induced by source paper "learning to compute word embeddings on the fly"):
  with members:
    - "dynamic integration of background knowledge in neural nlu systems"
    - "vocabulary selection strategies for neural machine translation"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "leveraging lexical resources for learning entity embeddings in multi relational data"
    - "enhancing and combining sequential and tree lstm for natural language inference"
    - "squad 100 000 questions for machine comprehension of text"
    - "vocabulary manipulation for neural machine translation"
    - "dynamic coattention networks for question answering"

Connected through hyperedge HE-4 (induced by source paper "an investigation of the interactions between pre trained word embeddings character models and pos tags in dependency parsing"):
  with members:
    - "attention is all you need"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "character aware neural language models"
    - "neural machine translation of rare words with subword units"
    - "transition based dependency parsing with stack long short term memory"

Connected through hyperedge HE-5 (induced by source paper "an exploration of word embedding initialization in deep learning tasks"):
  with members:
    - "subgram extending skip gram word representation with substrings"
    - "convolutional sequence to sequence learning"
    - "fully character level neural machine translation without explicit segmentation"
    - "neural architectures for named entity recognition"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"
    - "neural machine translation of rare words with subword units"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)