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: "clicr a dataset of clinical case reports for machine reading comprehension"
  Abstract: We present a new dataset for machine comprehension in the medical domain. Our dataset uses clinical case reports with around 100,000 gap-filling queries about these cases. We apply several baselines and state-of-the-art neural readers to the dataset, and observe a considerable gap in performance (20% F1) between the best human and machine readers. We analyze the skills required for successful answering and show how reader performance varies depending on the applicable skills. We find that inferences using domain knowledge and object tracking are the most frequently required skills, and that recognizing omitted information and spatio-temporal reasoning are the most difficult for the machines.

Connected through hyperedge HE-1 (induced by source paper "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"):
  with members:
    - "from recognition to cognition visual commonsense reasoning"
    - "annotation artifacts in natural language inference data"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "the web as a knowledge base for answering complex questions"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-2 (induced by source paper "unsupervised domain adaptation of language models for reading comprehension"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "learning and evaluating general linguistic intelligence"
    - "simplified neural unsupervised domain adaptation"
    - "adagraph unifying predictive and continuous domain adaptation through graphs"
    - "multi task deep neural networks for natural language understanding"
    - "mrqa 2019 shared task evaluating generalization in reading comprehension"
    - "zero shot domain adaptation based on attribute information"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-3 (induced by source paper "why can t memory networks read effectively"):
  with members:
    - "understanding dataset design choices for multi hop reasoning"
    - "misleading failures of partial input baselines"
    - "blindfold baselines for embodied qa"
    - "compositional attention networks for machine reasoning"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "constructing datasets for multi hop reading comprehension across documents"

Connected through hyperedge HE-4 (induced by source paper "extracting factual min max age information from clinical trial studies"):
  with members:
    - "scientific information extraction with semi supervised neural tagging"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-5 (induced by source paper "what makes reading comprehension questions easier"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "know what you don t know unanswerable questions for squad"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "making neural qa as simple as possible but not simpler"
    - "the narrativeqa reading comprehension challenge"

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)