human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "natural language inference over interaction space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial nli a new benchmark for natural language understanding")
  Member "memen multi layer embedding with memory networks for machine comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "supervised learning of universal sentence representations from natural language inference data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on contextual embeddings")
  Member "a compare propagate architecture with alignment factorization for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "several experiments on investigating pretraining and knowledge enhanced models for natural language inference")
  Member "discourse based objectives for fast unsupervised sentence representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pre training tasks for embedding based large scale retrieval")
  Member "dissent sentence representation learning from explicit discourse relations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probing what different nlp tasks teach machines about function word comprehension")
  Member "learned in translation contextualized word vectors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")
  Member "shortcut stacked sentence encoders for multi domain inference":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "stress test evaluation of transformer based models in natural language understanding tasks")

Question: Please tell me which class the source paper 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)