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: "discourse marker augmented network with reinforcement learning for natural language inference"
  Abstract: Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is one of the most important problems in natural language processing. It requires to infer the logical relationship between two given sentences. While current approaches mostly focus on the interaction architectures of the sentences, in this paper, we propose to transfer knowledge from some important discourse markers to augment the quality of the NLI model. We observe that people usually use some discourse markers such as "so" or "but" to represent the logical relationship between two sentences. These words potentially have deep connections with the meanings of the sentences, thus can be utilized to help improve the representations of them. Moreover, we use reinforcement learning to optimize a new objective function with a reward defined by the property of the NLI datasets to make full use of the labels information. Experiments show that our method achieves the state-of-the-art performance on several large-scale datasets.

Connected through hyperedge HE-1 (induced by source paper "bi decoder augmented network for neural machine translation"):
  with members:
    - "reinforced dynamic reasoning for conversational question generation"
    - "macnet transferring knowledge from machine comprehension to sequence to sequence models"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "attention is all you need"
    - "unsupervised neural machine translation"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "what do neural machine translation models learn about morphology"
    - "neural machine translation with reconstruction"

Connected through hyperedge HE-2 (induced by source paper "evaluation benchmarks and learning criteria for discourse aware sentence representations"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "enteval a holistic evaluation benchmark for entity representations"
    - "a unified linear time framework for sentence level discourse parsing"
    - "evaluating discourse in structured text representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "linguistic knowledge and transferability of contextual representations"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-3 (induced by source paper "macnet transferring knowledge from machine comprehension to sequence to sequence models"):
  with members:
    - "soft layer specific multi task summarization with entailment and question generation"
    - "selective encoding for abstractive sentence summarization"
    - "reinforced mnemonic reader for machine comprehension"
    - "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 "integrate image representation to text model on sentence level a semi supervised framework"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "i know what you want semantic learning for text comprehension"
    - "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning visually grounded sentence representations"
    - "vse improving visual semantic embeddings with hard negatives"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "effective subword segmentation for text comprehension"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "dual co matching network for multi choice reading comprehension"
    - "i know what you want semantic learning for text comprehension"
    - "graph based bilingual word embedding for statistical machine translation"
    - "open vocabulary learning for neural chinese pinyin ime"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"

Connected through hyperedge HE-6 (induced by source paper "combining similarity features and deep representation learning for stance detection in the context of checking fake news"):
  with members:
    - "universal transformers"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "automatic stance detection using end to end memory networks"
    - "co stack residual affinity networks with multi level attention refinement for matching text sequences"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "debunking fake news one feature at a time"
    - "universal sentence encoder"

Connected through hyperedge HE-7 (induced by source paper "i know what you want semantic learning for text comprehension"):
  with members:
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "know what you don t know unanswerable questions for squad"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "large scale qa srl parsing"
    - "multi passage machine reading comprehension with cross passage answer verification"

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)