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: "question answering through transfer learning from large fine grained supervision data"
  Abstract: We show that the task of question answering (QA) can significantly benefit from the transfer learning of models trained on a different large, fine-grained QA dataset. We achieve the state of the art in two well-studied QA datasets, WikiQA and SemEval-2016 (Task 3A), through a basic transfer learning technique from SQuAD. For WikiQA, our model outperforms the previous best model by more than 8%. We demonstrate that finer supervision provides better guidance for learning lexical and syntactic information than coarser supervision, through quantitative results and visual analysis. We also show that a similar transfer learning procedure achieves the state of the art on an entailment task.

Connected through hyperedge HE-1 (induced by source paper "reclor a reading comprehension dataset requiring logical reasoning"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "mmm multi stage multi task learning for multi choice reading comprehension"
    - "evalai towards better evaluation systems for ai agents"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cosmos qa machine reading comprehension with contextual commonsense reasoning"
    - "probing neural network comprehension of natural language arguments"

Connected through hyperedge HE-2 (induced by source paper "reasoning driven question answering for natural language understanding"):
  with members:
    - "on the capabilities and limitations of reasoning for natural language understanding"
    - "evidence sentence extraction for machine reading comprehension"
    - "question answering as global reasoning over semantic abstractions"
    - "going on a vacation takes longer than going for a walk a study of temporal commonsense understanding"
    - "cogcomptime a tool for understanding time in natural language text"
    - "annotation artifacts in natural language inference data"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "multi hop knowledge graph reasoning with reward shaping"

Connected through hyperedge HE-3 (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-4 (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-5 (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-6 (induced by source paper "cosmos qa machine reading comprehension with contextual commonsense reasoning"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "socialiqa commonsense reasoning about social interactions"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "dual co matching network for multi choice reading comprehension"
    - "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "annotation artifacts in natural language inference data"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"

Connected through hyperedge HE-7 (induced by source paper "adafilter adaptive filter fine tuning for deep transfer learning"):
  with members:
    - "depthwise convolution is all you need for learning multiple visual domains"
    - "transferable end to end aspect based sentiment analysis with selective adversarial learning"
    - "continual lifelong learning with neural networks a review"
    - "do better imagenet models transfer better"
    - "a survey on methods and theories of quantized neural networks"
    - "spottune transfer learning through adaptive fine tuning"
    - "co regularized alignment for unsupervised domain adaptation"
    - "explicit inductive bias for transfer learning with convolutional networks"

Connected through hyperedge HE-8 (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"
    - "attention is all you need"

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)