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: "dynamic fusion networks for machine reading comprehension"
  Abstract: This paper presents a novel neural model - Dynamic Fusion Network (DFN), for machine reading comprehension (MRC). DFNs differ from most state-of-the-art models in their use of a dynamic multi-strategy attention process, in which passages, questions and answer candidates are jointly fused into attention vectors, along with a dynamic multi-step reasoning module for generating answers. With the use of reinforcement learning, for each input sample that consists of a question, a passage and a list of candidate answers, an instance of DFN with a sample-specific network architecture can be dynamically constructed by determining what attention strategy to apply and how many reasoning steps to take. Experiments show that DFNs achieve the best result reported on RACE, a challenging MRC dataset that contains real human reading questions in a wide variety of types. A detailed empirical analysis also demonstrates that DFNs can produce attention vectors that summarize information from questions, passages and answer candidates more effectively than other popular MRC models.

Connected through hyperedge HE-1 (induced by source paper "evidence sentence extraction for machine reading comprehension"):
  with members:
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "training a ranking function for open domain question answering"
    - "ukp athene multi sentence textual entailment for claim verification"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "neural models for reasoning over multiple mentions using coreference"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "deep probabilistic logic a unifying framework for indirect supervision"

Connected through hyperedge HE-2 (induced by source paper "convolutional spatial attention model for reading comprehension with multiple choice questions"):
  with members:
    - "eliminet a model for eliminating options for reading comprehension with multiple choice questions"
    - "deep contextualized word representations"
    - "hfl rc system at semeval 2018 task 11 hybrid multi aspects model for commonsense reading comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "reinforced mnemonic reader for machine comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "attention over attention neural networks for reading comprehension"
    - "fusionnet fusing via fully aware attention with application to machine comprehension"

Connected through hyperedge HE-3 (induced by source paper "multi task learning for machine reading comprehension"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "a question focused multi factor attention network for question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "the natural language decathlon multitask learning as question answering"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "qanet combining local convolution with global self attention for reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "improving machine reading comprehension with general reading strategies"):
  with members:
    - "eliminet a model for eliminating options for reading comprehension with multiple choice questions"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "generating wikipedia by summarizing long sequences"
    - "what makes reading comprehension questions easier"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "coqa a conversational question answering challenge"
    - "multi range reasoning for machine comprehension"
    - "hfl rc system at semeval 2018 task 11 hybrid multi aspects model for commonsense reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "multi task learning with sample re weighting for machine reading comprehension"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "the natural language decathlon multitask learning as question answering"
    - "deep contextualized word representations"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "stochastic answer networks for squad 2 0"

Connected through hyperedge HE-6 (induced by source paper "improving question answering by commonsense based pre training"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension"
    - "a systematic classification of knowledge reasoning and context within the arc dataset"
    - "learning beyond datasets knowledge graph augmented neural networks for natural language processing"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "kg 2 learning to reason science exam questions with contextual knowledge graph embeddings"
    - "think you have solved question answering try arc the ai2 reasoning challenge"

Connected through hyperedge HE-7 (induced by source paper "multi range reasoning for machine comprehension"):
  with members:
    - "simple recurrent units for highly parallelizable recurrence"
    - "reinforced mnemonic reader for machine comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention is all you need"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "r 3 reinforced reader ranker for open domain question answering"
    - "attention over attention neural networks for reading comprehension"

Connected through hyperedge HE-8 (induced by source paper "a co matching model for multi choice reading comprehension"):
  with members:
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "race large scale reading comprehension dataset from examinations"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "attention based convolutional neural network for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "ms marco a human generated machine reading comprehension dataset"
    - "a compare aggregate model for matching text sequences"
    - "gated attention readers for text comprehension"

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)