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: "synthetic qa corpora generation with roundtrip consistency"
  Abstract: We introduce a novel method of generating synthetic question answering corpora by combining models of question generation and answer extraction, and by filtering the results to ensure roundtrip consistency. By pretraining on the resulting corpora we obtain significant improvements on SQuAD2 and NQ, establishing a new state-of-the-art on the latter. Our synthetic data generation models, for both question generation and answer extraction, can be fully reproduced by finetuning a publicly available BERT model on the extractive subsets of SQuAD2 and NQ. We also describe a more powerful variant that does full sequence-to-sequence pretraining for question generation, obtaining exact match and F1 at less than 0.1% and 0.4% from human performance on SQuAD2.

Connected through hyperedge HE-1 (induced by source paper "addressing semantic drift in question generation for semi supervised question answering"):
  with members:
    - "learning to generate questions by learningwhat not to generate"
    - "evaluating rewards for question generation models"
    - "unified language model pre training for natural language understanding and generation"
    - "learning to navigate unseen environments back translation with environmental dropout"
    - "towards a better metric for evaluating question generation systems"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "speaker follower models for vision and language navigation"

Connected through hyperedge HE-2 (induced by source paper "ask to learn a study on curiosity driven question generation"):
  with members:
    - "question answering as an automatic evaluation metric for news article summarization"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "evaluating rewards for question generation models"
    - "unified language model pre training for natural language understanding and generation"
    - "analyzing uncertainty in neural machine translation"
    - "towards a better metric for evaluating question generation systems"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "mlqa evaluating cross lingual extractive question answering"):
  with members:
    - "neural arabic question answering"
    - "cross lingual transfer learning for question answering"
    - "beyond english only reading comprehension experiments in zero shot multilingual transfer for bulgarian"
    - "unsupervised question answering by cloze translation"
    - "xcmrc evaluating cross lingual machine reading comprehension"
    - "korquad1 0 korean qa dataset for machine reading comprehension"
    - "xlda cross lingual data augmentation for natural language inference and question answering"
    - "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"

Connected through hyperedge HE-4 (induced by source paper "undersensitivity in neural reading comprehension"):
  with members:
    - "learning to ask unanswerable questions for machine reading comprehension"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "roberta a robustly optimized bert pretraining approach"
    - "robust machine comprehension models via adversarial training"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "what makes reading comprehension questions easier"
    - "generating natural language adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "a logic driven framework for consistency of neural models"):
  with members:
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "augmenting neural networks with first order logic"
    - "be consistent improving procedural text comprehension using label consistency"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "analyzing compositionality sensitivity of nli models"

Connected through hyperedge HE-6 (induced by source paper "frustratingly easy natural question answering"):
  with members:
    - "span selection pre training for question answering"
    - "sg net syntax guided machine reading comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "a bert baseline for the natural questions"
    - "roberta a robustly optimized bert pretraining approach"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-7 (induced by source paper "meta answering for machine reading"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "answering while summarizing multi task learning for multi hop qa with evidence extraction"
    - "revealing the importance of semantic retrieval for machine reading at scale"
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "a bert baseline for the natural questions"
    - "probing neural network comprehension of natural language arguments"
    - "episodic memory reader learning what to remember for question answering from streaming data"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-8 (induced by source paper "span selection pre training for question answering"):
  with members:
    - "spanbert improving pre training by representing and predicting spans"
    - "ernie 2 0 a continual pre training framework for language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "know what you don t know unanswerable questions for squad"

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)