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: "natural language comprehension with the epireader"
  Abstract: We present the EpiReader, a novel model for machine comprehension of text. Machine comprehension of unstructured, real-world text is a major research goal for natural language processing. Current tests of machine comprehension pose questions whose answers can be inferred from some supporting text, and evaluate a model's response to the questions. The EpiReader is an end-to-end neural model comprising two components: the first component proposes a small set of candidate answers after comparing a question to its supporting text, and the second component formulates hypotheses using the proposed candidates and the question, then reranks the hypotheses based on their estimated concordance with the supporting text. We present experiments demonstrating that the EpiReader sets a new state-of-the-art on the CNN and Children's Book Test machine comprehension benchmarks, outperforming previous neural models by a significant margin.

Connected through hyperedge HE-1 (induced by source paper "saliency learning teaching the model where to pay attention"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "event detection with neural networks a rigorous empirical evaluation"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "dependent gated reading for cloze style question answering"
    - "event nugget detection with forward backward recurrent neural networks"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "analyzing the behavior of visual question answering models"
    - "text understanding with the attention sum reader network"

Connected through hyperedge HE-2 (induced by source paper "design and challenges of cloze style reading comprehension tasks on multiparty dialogue"):
  with members:
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention over attention neural networks for reading comprehension"
    - "cross genre document retrieval matching between conversational and formal writings"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-3 (induced by source paper "effective character augmented word embedding for machine reading comprehension"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "lingke a fine grained multi turn chatbot for customer service"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "subword augmented embedding for cloze reading comprehension"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "deep contextualized word representations"
    - "one shot learning for question answering in gaokao history challenge"
    - "sjtu nlp at semeval 2018 task 9 neural hypernym discovery with term embeddings"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "character word lstm language models"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "fast and accurate neural word segmentation for chinese"

Connected through hyperedge HE-5 (induced by source paper "joint training of candidate extraction and answer selection for reading comprehension"):
  with members:
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "reinforced mnemonic reader for machine comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "r 3 reinforced reader ranker for open domain question answering"
    - "quasar datasets for question answering by search and reading"
    - "learning recurrent span representations for extractive question answering"
    - "ask the right questions active question reformulation with reinforcement learning"
    - "reasonet learning to stop reading in machine comprehension"

Connected through hyperedge HE-6 (induced by source paper "entity tracking improves cloze style reading comprehension"):
  with members:
    - "neural models for reasoning over multiple mentions using coreference"
    - "attention over attention neural networks for reading comprehension"
    - "linguistic knowledge as memory for recurrent neural networks"
    - "broad context language modeling as reading comprehension"
    - "text understanding with the attention sum reader network"
    - "the lambada dataset word prediction requiring a broad discourse context"
    - "learning global features for coreference resolution"
    - "tracking the world state with recurrent entity networks"

Connected through hyperedge HE-7 (induced by source paper "medical exam question answering with large scale reading comprehension"):
  with members:
    - "attention over attention neural networks for reading comprehension"
    - "dataset and neural recurrent sequence labeling model for open domain factoid question answering"
    - "text understanding with the attention sum reader network"
    - "squad 100 000 questions for machine comprehension of text"
    - "iterative alternating neural attention for machine reading"
    - "key value memory networks for directly reading documents"
    - "ms marco a human generated machine reading comprehension dataset"
    - "dynamic coattention networks for question answering"

Connected through hyperedge HE-8 (induced by source paper "dependent gated reading for cloze style question answering"):
  with members:
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "tweet2vec character based distributed representations for social media"
    - "dataset and neural recurrent sequence labeling model for open domain factoid question answering"
    - "text understanding with the attention sum reader network"
    - "iterative alternating neural attention for machine reading"
    - "who did what a large scale person centered cloze dataset"
    - "gated attention readers for text comprehension"
    - "finding function in form compositional character models for open vocabulary word representation"

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)