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: "attention guided answer distillation for machine reading comprehension"
  Abstract: Despite that current reading comprehension systems have achieved significant advancements, their promising performances are often obtained at the cost of making an ensemble of numerous models. Besides, existing approaches are also vulnerable to adversarial attacks. This paper tackles these problems by leveraging knowledge distillation, which aims to transfer knowledge from an ensemble model to a single model. We first demonstrate that vanilla knowledge distillation applied to answer span prediction is effective for reading comprehension systems. We then propose two novel approaches that not only penalize the prediction on confusing answers but also guide the training with alignment information distilled from the ensemble. Experiments show that our best student model has only a slight drop of 0.4% F1 on the SQuAD test set compared to the ensemble teacher, while running 12x faster during inference. It even outperforms the teacher on adversarial SQuAD datasets and NarrativeQA benchmark.

Connected through hyperedge HE-1 (induced by source paper "a study of the tasks and models in machine reading comprehension"):
  with members:
    - "a deep cascade model for multi document reading comprehension"
    - "multi style generative reading comprehension"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (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-3 (induced by source paper "knowledge squeezed adversarial network compression"):
  with members:
    - "ktan knowledge transfer adversarial network"
    - "model compression via distillation and quantization"
    - "learn to pay attention"
    - "adversarial network compression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"
    - "incremental network quantization towards lossless cnns with low precision weights"

Connected through hyperedge HE-4 (induced by source paper "efficient video classification using fewer frames"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "non local netvlad encoding for video classification"
    - "constrained size tensorflow models for youtube 8m video understanding challenge"
    - "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"
    - "knowledge distillation by on the fly native ensemble"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "learnable pooling methods for video classification"
    - "label denoising with large ensembles of heterogeneous neural networks"

Connected through hyperedge HE-5 (induced by source paper "simple and effective curriculum pointer generator networks for reading comprehension over long narratives"):
  with members:
    - "efficient and robust question answering from minimal context over documents"
    - "the natural language decathlon multitask learning as question answering"
    - "automated curriculum learning for neural networks"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "attention is all you need"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "get to the point summarization with pointer generator networks"
    - "the narrativeqa reading comprehension challenge"

Connected through hyperedge HE-6 (induced by source paper "well read students learn better on the importance of pre training compact models"):
  with members:
    - "model compression with multi task knowledge distillation for web scale question answering system"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "bam born again multi task networks for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "patient knowledge distillation for bert model compression"
    - "roberta a robustly optimized bert pretraining approach"
    - "annotation artifacts in natural language inference data"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-7 (induced by source paper "tinybert distilling bert for natural language understanding"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "spanbert improving pre training by representing and predicting spans"
    - "revealing the dark secrets of bert"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "patient knowledge distillation for bert model compression"
    - "what does bert look at an analysis of bert s attention"

Connected through hyperedge HE-8 (induced by source paper "hint based training for non autoregressive machine translation"):
  with members:
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "fast decoding in sequence models using discrete latent variables"
    - "towards neural phrase based machine translation"
    - "attention is all you need"
    - "non autoregressive neural machine translation"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "sequence level knowledge distillation"

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)