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: "supervised and unsupervised transfer learning for question answering"
  Abstract: Although transfer learning has been shown to be successful for tasks like object and speech recognition, its applicability to question answering (QA) has yet to be well-studied. In this paper, we conduct extensive experiments to investigate the transferability of knowledge learned from a source QA dataset to a target dataset using two QA models. The performance of both models on a TOEFL listening comprehension test (Tseng et al., 2016) and MCTest (Richardson et al., 2013) is significantly improved via a simple transfer learning technique from MovieQA (Tapaswi et al., 2016). In particular, one of the models achieves the state-of-the-art on all target datasets; for the TOEFL listening comprehension test, it outperforms the previous best model by 7%. Finally, we show that transfer learning is helpful even in unsupervised scenarios when correct answers for target QA dataset examples are not available.

Connected through hyperedge HE-1 (induced by source paper "quick and not so dirty unsupervised selection of justification sentences for multi hop question answering"):
  with members:
    - "improving question answering with external knowledge"
    - "understanding dataset design choices for multi hop reasoning"
    - "evidence sentence extraction for machine reading comprehension"
    - "question answering as global reasoning over semantic abstractions"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "repurposing entailment for multi hop question answering tasks"
    - "an interpretable reasoning network for multi relation question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"

Connected through hyperedge HE-2 (induced by source paper "adversarial domain adaptation for machine reading comprehension"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "adversarial domain adaptation for duplicate question detection"
    - "multi task learning with sample re weighting for machine reading comprehension"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "deep contextualized word representations"
    - "neural approaches to conversational ai"
    - "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-3 (induced by source paper "multiqa an empirical investigation of generalization and transfer in reading comprehension"):
  with members:
    - "multi step retriever reader interaction for scalable open domain question answering"
    - "learning and evaluating general linguistic intelligence"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "multi task deep neural networks for natural language understanding"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "an embarrassingly simple approach for transfer learning from pretrained language models"
    - "densely connected attention propagation for reading comprehension"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"

Connected through hyperedge HE-4 (induced by source paper "cross lingual transfer learning for question answering"):
  with members:
    - "polyglot contextual representations improve crosslingual transfer"
    - "cross lingual language model pretraining"
    - "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "multilingual extractive reading comprehension by runtime machine translation"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "drcd a chinese machine reading comprehension dataset"

Connected through hyperedge HE-5 (induced by source paper "mmm multi stage multi task learning for multi choice reading comprehension"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "dynamically fused graph network for multi hop reasoning"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "roberta a robustly optimized bert pretraining approach"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-6 (induced by source paper "unsupervised domain adaptation on reading comprehension"):
  with members:
    - "learning and evaluating general linguistic intelligence"
    - "multi task deep neural networks for natural language understanding"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "coqa a conversational question answering challenge"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "question answering through transfer learning from large fine grained supervision data"

Connected through hyperedge HE-7 (induced by source paper "hashing based answer selection"):
  with members:
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "question answering through transfer learning from large fine grained supervision data"
    - "attention is all you need"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "neural domain adaptation for biomedical question answering"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"
    - "hashnet deep learning to hash by continuation"

Connected through hyperedge HE-8 (induced by source paper "a gated self attention memory network for answer selection"):
  with members:
    - "a compare aggregate model with latent clustering for answer selection"
    - "deep contextualized word representations"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "question answering through transfer learning from large fine grained supervision data"
    - "attention is all you need"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "bilateral multi perspective matching for natural language sentences"

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)