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: "multi range reasoning for machine comprehension"
  Abstract: We propose MRU (Multi-Range Reasoning Units), a new fast compositional encoder for machine comprehension (MC). Our proposed MRU encoders are characterized by multi-ranged gating, executing a series of parameterized contract-and-expand layers for learning gating vectors that benefit from long and short-term dependencies. The aims of our approach are as follows: (1) learning representations that are concurrently aware of long and short-term context, (2) modeling relationships between intra-document blocks and (3) fast and efficient sequence encoding. We show that our proposed encoder demonstrates promising results both as a standalone encoder and as well as a complementary building block. We conduct extensive experiments on three challenging MC datasets, namely RACE, SearchQA and NarrativeQA, achieving highly competitive performance on all. On the RACE benchmark, our model outperforms DFN (Dynamic Fusion Networks) by 1.5%-6% without using any recurrent or convolution layers. Similarly, we achieve competitive performance relative to AMANDA on the SearchQA benchmark and BiDAF on the NarrativeQA benchmark without using any LSTM/GRU layers. Finally, incorporating MRU encoders with standard BiLSTM architectures further improves performance, achieving state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "dual multi head co attention for multi choice reading comprehension"):
  with members:
    - "a survey on machine reading comprehension systems"
    - "multi task learning with multi head attention for multi choice reading comprehension"
    - "option comparison network for multiple choice reading comprehension"
    - "mmm multi stage multi task learning for multi choice reading comprehension"
    - "albert a lite bert for self supervised learning of language representations"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-2 (induced by source paper "beyond english only reading comprehension experiments in zero shot multilingual transfer for bulgarian"):
  with members:
    - "passage re ranking with bert"
    - "improving question answering with external knowledge"
    - "transformer xl attentive language models beyond a fixed length context"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"

Connected through hyperedge HE-3 (induced by source paper "dual co matching network for multi choice reading comprehension"):
  with members:
    - "semantics aware bert for language understanding"
    - "hierarchical contextualized representation for named entity recognition"
    - "convolutional spatial attention model for reading comprehension with multiple choice questions"
    - "option comparison network for multiple choice reading comprehension"
    - "sg net syntax guided machine reading comprehension"
    - "evidence sentence extraction for machine reading comprehension"
    - "lattice based transformer encoder for neural machine translation"
    - "head driven phrase structure grammar parsing on penn treebank"

Connected through hyperedge HE-4 (induced by source paper "dream a challenge dataset and models for dialogue based reading comprehension"):
  with members:
    - "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
    - "coqa a conversational question answering challenge"
    - "odsqa open domain spoken question answering dataset"
    - "quac question answering in context"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "verb physics relative physical knowledge of actions and objects"
    - "the narrativeqa reading comprehension challenge"
    - "embracing data abundance booktest dataset for reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "option comparison network for multiple choice reading comprehension"):
  with members:
    - "eliminet a model for eliminating options for reading comprehension with multiple choice questions"
    - "deep contextualized word representations"
    - "improving machine reading comprehension with general reading strategies"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "recurrently controlled recurrent networks"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "co stack residual affinity networks with multi level attention refinement for matching text sequences"
    - "densely connected bidirectional lstm with applications to sentence classification"
    - "deep contextualized word representations"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "learning to generate reviews and discovering sentiment"
    - "sentence state lstm for text representation"
    - "simple recurrent units for highly parallelizable recurrence"

Connected through hyperedge HE-7 (induced by source paper "densely connected attention propagation for reading comprehension"):
  with members:
    - "a question focused multi factor attention network for question answering"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "densely connected bidirectional lstm with applications to sentence classification"
    - "attention guided answer distillation for machine reading comprehension"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evidence aggregation for answer re ranking in open domain question answering"

Connected through hyperedge HE-8 (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"
    - "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"

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)