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: "an empirical analysis of multiple turn reasoning strategies in reading comprehension tasks"
  Abstract: Reading comprehension (RC) is a challenging task that requires synthesis of information across sentences and multiple turns of reasoning. Using a state-of-the-art RC model, we empirically investigate the performance of single-turn and multiple-turn reasoning on the SQuAD and MS MARCO datasets. The RC model is an end-to-end neural network with iterative attention, and uses reinforcement learning to dynamically control the number of turns. We find that multiple-turn reasoning outperforms single-turn reasoning for all question and answer types; further, we observe that enabling a flexible number of turns generally improves upon a fixed multiple-turn strategy. %across all question types, and is particularly beneficial to questions with lengthy, descriptive answers. We achieve results competitive to the state-of-the-art on these two datasets.

Connected through hyperedge HE-1 (induced by source paper "multi task learning for machine reading comprehension"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "a question focused multi factor attention network for question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "the natural language decathlon multitask learning as question answering"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "qanet combining local convolution with global self attention for reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "multi task learning with sample re weighting for machine reading comprehension"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "the natural language decathlon multitask learning as question answering"
    - "deep contextualized word representations"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "stochastic answer networks for squad 2 0"

Connected through hyperedge HE-3 (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"
    - "multi task learning for machine reading comprehension"
    - "natural language inference over interaction space"
    - "attention is all you need"
    - "stochastic answer networks for machine reading comprehension"
    - "neural paraphrase identification of questions with noisy pretraining"

Connected through hyperedge HE-4 (induced by source paper "stochastic answer networks for machine reading comprehension"):
  with members:
    - "phase conductor on multi layered attentions for machine comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "stochastic answer networks for natural language inference"
    - "reinforced mnemonic reader for machine comprehension"
    - "attention is all you need"
    - "learned in translation contextualized word vectors"
    - "learning recurrent span representations for extractive question answering"
    - "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)