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: "a thorough examination of the cnn daily mail reading comprehension task"
  Abstract: Enabling a computer to understand a document so that it can answer comprehension questions is a central, yet unsolved goal of NLP. A key factor impeding its solution by machine learned systems is the limited availability of human-annotated data. Hermann et al. (2015) seek to solve this problem by creating over a million training examples by pairing CNN and Daily Mail news articles with their summarized bullet points, and show that a neural network can then be trained to give good performance on this task. In this paper, we conduct a thorough examination of this new reading comprehension task. Our primary aim is to understand what depth of language understanding is required to do well on this task. We approach this from one side by doing a careful hand-analysis of a small subset of the problems and from the other by showing that simple, carefully designed systems can obtain accuracies of 73.6% and 76.6% on these two datasets, exceeding current state-of-the-art results by 7-10% and approaching what we believe is the ceiling for performance on this task.

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"
    - "attention guided answer distillation for machine reading comprehension"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-2 (induced by source paper "beat the ai investigating adversarial human annotations for reading comprehension"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "assessing the benchmarking capacity of machine reading comprehension datasets"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "learning the difference that makes a difference with counterfactually augmented data"
    - "adversarial nli a new benchmark for natural language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "annotation artifacts in natural language inference data"

Connected through hyperedge HE-3 (induced by source paper "a pilot study on multiple choice machine reading comprehension for vietnamese texts"):
  with members:
    - "vietnamese transition based dependency parsing with supertag features"
    - "semeval 2017 task 3 community question answering"
    - "korquad1 0 korean qa dataset for machine reading comprehension"
    - "error analysis for vietnamese named entity recognition on deep neural network models"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "sberquad russian reading comprehension dataset description and analysis"
    - "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
    - "lstm easy first dependency parsing with pre trained word embeddings and character level word embeddings in vietnamese"

Connected through hyperedge HE-4 (induced by source paper "gennet reading comprehension with multiple choice questions using generation and selection model"):
  with members:
    - "eliminet a model for eliminating options for reading comprehension with multiple choice questions"
    - "convolutional spatial attention model for reading comprehension with multiple choice questions"
    - "option comparison network for multiple choice reading comprehension"
    - "dual co matching network for multi choice reading comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "a co matching model for multi choice reading comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "s net from answer extraction to answer generation for machine reading comprehension"

Connected through hyperedge HE-5 (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-6 (induced by source paper "structure invariant testing for machine translation"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "machine learning testing survey landscapes and horizons"
    - "defensive quantization when efficiency meets robustness"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "achieving human parity on automatic chinese to english news translation"
    - "code2vec learning distributed representations of code"
    - "on adversarial examples for character level neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "neural text summarization a critical evaluation"):
  with members:
    - "what comes next extractive summarization by next sentence prediction"
    - "neural extractive text summarization with syntactic compression"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "content selection in deep learning models of summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "annotation artifacts in natural language inference data"
    - "generating wikipedia by summarizing long sequences"

Connected through hyperedge HE-8 (induced by source paper "quizbowl the case for incremental question answering"):
  with members:
    - "multinomial adversarial networks for multi domain text classification"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "what can ai do for me evaluating machine learning interpretations in cooperative play"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "what makes reading comprehension questions easier"
    - "studio ousia s quiz bowl question answering system"
    - "deep contextualized word representations"

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)