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: "learning to ask unanswerable questions for machine reading comprehension"
  Abstract: Machine reading comprehension with unanswerable questions is a challenging task. In this work, we propose a data augmentation technique by automatically generating relevant unanswerable questions according to an answerable question paired with its corresponding paragraph that contains the answer. We introduce a pair-to-sequence model for unanswerable question generation, which effectively captures the interactions between the question and the paragraph. We also present a way to construct training data for our question generation models by leveraging the existing reading comprehension dataset. Experimental results show that the pair-to-sequence model performs consistently better compared with the sequence-to-sequence baseline. We further use the automatically generated unanswerable questions as a means of data augmentation on the SQuAD 2.0 dataset, yielding 1.9 absolute F1 improvement with BERT-base model and 1.7 absolute F1 improvement with BERT-large model.

Connected through hyperedge HE-1 (induced by source paper "undersensitivity in neural reading comprehension"):
  with members:
    - "synthetic qa corpora generation with roundtrip consistency"
    - "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-2 (induced by source paper "lstm sharp an adaptable energy efficient hardware accelerator for long short term memory"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "the emergence of number and syntax units in lstm language models"
    - "recent advances in recurrent neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "bidirectional attention flow for machine comprehension"
    - "optimizing performance of recurrent neural networks on gpus"
    - "eie efficient inference engine on compressed deep neural network"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-3 (induced by source paper "orb an open reading benchmark for comprehensive evaluation of machine reading comprehension"):
  with members:
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "bert and pals projected attention layers for efficient adaptation in multi task learning"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "reasoning over paragraph effects in situations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "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)