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: "exploring question understanding and adaptation in neural network based question answering"
  Abstract: The last several years have seen intensive interest in exploring neural-network-based models for machine comprehension (MC) and question answering (QA). In this paper, we approach the problems by closely modelling questions in a neural network framework. We first introduce syntactic information to help encode questions. We then view and model different types of questions and the information shared among them as an adaptation task and proposed adaptation models for them. On the Stanford Question Answering Dataset (SQuAD), we show that these approaches can help attain better results over a competitive baseline.

Connected through hyperedge HE-1 (induced by source paper "towards domain adaptation from limited data for question answering using deep neural networks"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "multi task deep neural networks for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "adversarial domain adaptation for machine reading comprehension"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep contextualized word representations"
    - "emrqa a large corpus for question answering on electronic medical records"

Connected through hyperedge HE-2 (induced by source paper "several experiments on investigating pretraining and knowledge enhanced models for natural language inference"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "enhancing sentence embedding with generalized pooling"
    - "dynamic self attention computing attention over words dynamically for sentence embedding"
    - "what if we simply swap the two text fragments a straightforward yet effective way to test the robustness of methods to confounding signals in nature language inference tasks"

Connected through hyperedge HE-3 (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"
    - "contextualized word representations for reading comprehension"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "enhancing sentence embedding with generalized pooling"):
  with members:
    - "fine grained attention mechanism for neural machine translation"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "neural natural language inference models enhanced with external knowledge"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "a compare propagate architecture with alignment factorization for natural language inference"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-5 (induced by source paper "multilingual extractive reading comprehension by runtime machine translation"):
  with members:
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "reinforced mnemonic reader for machine comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "making neural qa as simple as possible but not simpler"
    - "dcn mixed objective and deep residual coattention for question answering"
    - "reinforced mnemonic reader for machine reading comprehension"

Connected through hyperedge HE-6 (induced by source paper "what happened leveraging verbnet to predict the effects of actions in procedural text"):
  with members:
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "simulating action dynamics with neural process networks"
    - "adversarial examples for evaluating reading comprehension systems"
    - "squad 100 000 questions for machine comprehension of text"
    - "tracking the world state with recurrent entity networks"
    - "query reduction networks for question answering"
    - "teaching machines to read and comprehend"

Connected through hyperedge HE-7 (induced by source paper "phase conductor on multi layered attentions for machine comprehension"):
  with members:
    - "reinforced mnemonic reader for machine comprehension"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "attention is all you need"
    - "squad 100 000 questions for machine comprehension of text"
    - "dynamic coattention networks for question answering"
    - "machine comprehension using match lstm and answer pointer"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "reinforced mnemonic reader for machine comprehension"):
  with members:
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention is all you need"
    - "attention over attention neural networks for reading comprehension"
    - "structural embedding of syntactic trees for machine comprehension"
    - "stochastic answer networks for machine reading comprehension"
    - "making neural qa as simple as possible but not simpler"

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)