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: "multi task learning for machine reading comprehension"
  Abstract: We propose a multi-task learning framework to jointly train a Machine Reading Comprehension (MRC) model on multiple datasets across different domains. Key to the proposed method is to learn robust and general contextual representations with the help of out-domain data in a multi-task framework. Empirical study shows that the proposed approach is orthogonal to the existing pre-trained representation models, such as word embedding and language models. Experiments on the Stanford Question Answering Dataset (SQuAD), the Microsoft MAchine Reading COmprehension Dataset (MS MARCO), NewsQA and other datasets show that our multi-task learning approach achieves significant improvement over state-of-the-art models in most MRC tasks.

Connected through hyperedge HE-1 (induced by source paper "attentive history selection for conversational question answering"):
  with members:
    - "bert with history answer embedding for conversational question answering"
    - "answer interaction in non factoid question answering systems"
    - "multi task deep neural networks for natural language understanding"
    - "user intent prediction in information seeking conversations"
    - "a deep look into neural ranking models for information retrieval"
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "analyzing and characterizing user intent in information seeking conversations"

Connected through hyperedge HE-2 (induced by source paper "multi task deep neural networks for natural language understanding"):
  with members:
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "unified language model pre training for natural language understanding and generation"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "neural approaches to conversational ai"

Connected through hyperedge HE-3 (induced by source paper "frustratingly easy natural question answering"):
  with members:
    - "span selection pre training for question answering"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "improving multi task deep neural networks via knowledge distillation for natural language understanding"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "multi task deep neural networks for natural language understanding"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "neural approaches to conversational ai"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "stochastic answer networks for machine reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "stochastic answer networks for natural language inference"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "natural language inference over interaction space"
    - "an empirical analysis of multiple turn reasoning strategies in reading comprehension tasks"
    - "attention is all you need"
    - "stochastic answer networks for machine reading comprehension"
    - "neural paraphrase identification of questions with noisy pretraining"

Connected through hyperedge HE-6 (induced by source paper "stochastic answer networks for squad 2 0"):
  with members:
    - "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"
    - "read verify machine reading comprehension with unanswerable questions"
    - "stochastic answer networks for natural language inference"
    - "attention is all you need"
    - "learned in translation contextualized word vectors"
    - "a structured self attentive sentence embedding"

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)