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: "contextualized word representations for reading comprehension"
  Abstract: Reading a document and extracting an answer to a question about its content has attracted substantial attention recently. While most work has focused on the interaction between the question and the document, in this work we evaluate the importance of context when the question and document are processed independently. We take a standard neural architecture for this task, and show that by providing rich contextualized word representations from a large pre-trained language model as well as allowing the model to choose between context-dependent and context-independent word representations, we can obtain dramatic improvements and reach performance comparable to state-of-the-art on the competitive SQuAD dataset.

Connected through hyperedge HE-1 (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"
    - "smarnet teaching machines to read and comprehend like human"

Connected through hyperedge HE-2 (induced by source paper "harry potter and the action prediction challenge from natural language"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"
    - "whodunnit crime drama as a case for natural language understanding"
    - "attention is all you need"
    - "the narrativeqa reading comprehension challenge"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "squad 100 000 questions for machine comprehension of text"
    - "embracing data abundance booktest dataset for reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "from word to sense embeddings a survey on vector representations of meaning"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "seven augmenting word embeddings with unsupervised relation vectors"
    - "incorporating glosses into neural word sense disambiguation"
    - "deep contextualized word representations"
    - "wic 10 000 example pairs for evaluating context sensitive representations"
    - "knowledge based word sense disambiguation using topic models"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-4 (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"
    - "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"
    - "end to end answer chunk extraction and ranking for reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "learning to search in long documents using document structure"):
  with members:
    - "towards inference oriented reading comprehension parallelqa"
    - "rainbow combining improvements in deep reinforcement learning"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "neural speed reading via skim rnn"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "learning recurrent span representations for extractive question answering"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "the narrativeqa reading comprehension challenge"

Connected through hyperedge HE-6 (induced by source paper "exploring machine reading comprehension with explicit knowledge"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "densely connected attention propagation for reading comprehension"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "know what you don t know unanswerable questions for squad"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "explicit utilization of general knowledge in machine reading comprehension"):
  with members:
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "know what you don t know unanswerable questions for squad"
    - "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"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "structural embedding of syntactic trees for machine comprehension"

Connected through hyperedge HE-8 (induced by source paper "phrase indexed question answering a new challenge for scalable document comprehension"):
  with members:
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "deep contextualized word representations"
    - "reinforced mnemonic reader for machine comprehension"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention is all you need"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "billion scale similarity search with gpus"

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)