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 "interpreting recurrent and attention based neural models a case study on natural language inference"):
  Abstract: Deep learning models have achieved remarkable success in natural language inference (NLI) tasks. While these models are widely explored, they are hard to interpret and it is often unclear how and why they actually work. In this paper, we take a step toward explaining such deep learning based models through a case study on a popular neural model for NLI. In particular, we propose to interpret the intermediate layers of NLI models by visualizing the saliency of attention and LSTM gating signals. We present several examples for which our methods are able to reveal interesting insights and identify the critical information contributing to the model decisions.

Includes members:
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "dependent gated reading for cloze style question answering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "saliency learning teaching the model where to pay attention")
  Member "zero shot sequence labeling transferring knowledge from sentences to tokens":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sequence labeling parsing by learning across representations")
  Member "dr bilstm dependent reading bidirectional lstm for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "kernel graph attention network for fact verification")
  Member "natural language inference over interaction space":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial nli a new benchmark for natural language understanding")
  Member "explaining recurrent neural network predictions in sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sam the sensitivity of attribution methods to hyperparameters")
  Member "a compare propagate architecture with alignment factorization for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discourse marker augmented network with reinforcement learning for natural language inference")
  Member "learned in translation contextualized word vectors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on contextual embeddings")

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)