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: "reinforced mnemonic reader for machine comprehension"
  Abstract: In this paper, we introduce the Reinforced Mnemonic Reader for machine comprehension (MC) task, which aims to answer a query about a given context document. We propose several novel mechanisms that address critical problems in MC that are not adequately solved by previous works, such as enhancing the capacity of encoder, modeling long-term dependencies of contexts, refining the predicted answer span, and directly optimizing the evaluation metric. Extensive experiments on TriviaQA and Stanford Question Answering Dataset (SQuAD) show that our model achieves state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "conditional self attention for query based summarization"):
  with members:
    - "universal transformers"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "generating wikipedia by summarizing long sequences"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "enhancing sentence embedding with generalized pooling"
    - "neural natural language inference models enhanced with external knowledge"

Connected through hyperedge HE-2 (induced by source paper "coarse grain fine grain coattention network for multi evidence question answering"):
  with members:
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "efficient and robust question answering from minimal context over documents"
    - "neural models for reasoning over multiple mentions using coreference"
    - "global locally self attentive dialogue state tracker"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "coarse to fine decoding for neural semantic parsing"

Connected through hyperedge HE-3 (induced by source paper "macnet transferring knowledge from machine comprehension to sequence to sequence models"):
  with members:
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "selective encoding for abstractive sentence summarization"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "focal loss for dense object detection"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention over attention neural networks for reading comprehension"
    - "learned in translation contextualized word vectors"

Connected through hyperedge HE-4 (induced by source paper "cross lingual transfer learning for question answering"):
  with members:
    - "polyglot contextual representations improve crosslingual transfer"
    - "cross lingual language model pretraining"
    - "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "multilingual extractive reading comprehension by runtime machine translation"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "drcd a chinese machine reading comprehension dataset"

Connected through hyperedge HE-5 (induced by source paper "controlling risk of web question answering"):
  with members:
    - "densely connected attention propagation for reading comprehension"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "reducing network agnostophobia"
    - "confidence modeling for neural semantic parsing"
    - "know what you don t know unanswerable questions for squad"
    - "retrieve and read multi task learning of information retrieval and reading comprehension"
    - "bias reduced uncertainty estimation for deep neural classifiers"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "a deep cascade model for multi document reading comprehension"):
  with members:
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "smarnet teaching machines to read and comprehend like human"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "r 3 reinforced reader ranker for open domain question answering"

Connected through hyperedge HE-7 (induced by source paper "temporal self attention network for medical concept embedding"):
  with members:
    - "prototype propagation networks ppn for weakly supervised few shot learning on category graph"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "medical concept embedding with time aware attention"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "integrative analysis of patient health records and neuroimages via memory based graph convolutional network"
    - "scalable and accurate deep learning for electronic health records"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "has qa hierarchical answer spans model for open domain question answering"):
  with members:
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "quasar datasets for question answering by search and reading"
    - "making neural qa as simple as possible but not simpler"
    - "ask the right questions active question reformulation with reinforcement learning"
    - "s net from answer extraction to answer generation for machine reading comprehension"
    - "reading wikipedia to answer open domain questions"

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)