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 "an exploration of dropout with rnns for natural language inference"):
  Abstract: Dropout is a crucial regularization technique for the Recurrent Neural Network (RNN) models of Natural Language Inference (NLI). However, dropout has not been evaluated for the effectiveness at different layers and dropout rates in NLI models. In this paper, we propose a novel RNN model for NLI and empirically evaluate the effect of applying dropout at different layers in the model. We also investigate the impact of varying dropout rates at these layers. Our empirical evaluation on a large (Stanford Natural Language Inference (SNLI)) and a small (SciTail) dataset suggest that dropout at each feed-forward connection severely affects the model accuracy at increasing dropout rates. We also show that regularizing the embedding layer is efficient for SNLI whereas regularizing the recurrent layer improves the accuracy for SciTail. Our model achieved an accuracy \(86.14 \%\) on the SNLI dataset and \(77.05 \%\) on SciTail.

Includes members:
  Member "dr bilstm dependent reading bidirectional lstm for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "kernel graph attention network for fact verification")
  Member "neural natural language inference models enhanced with external knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sequential neural networks for noetic end to end response selection")
  Member "compare compress and propagate enhancing neural architectures with alignment factorization for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi level head wise match and aggregation in transformer for textual sequence matching")
  Member "learning to compose task specific tree structures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "long short term memory networks for machine reading":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "time aware large kernel convolutions")
  Member "learning natural language inference using bidirectional lstm model and inner attention":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mhsan multi head self attention network for visual semantic embedding")
  Member "a fast unified model for parsing and sentence understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "description based text classification with reinforcement learning")
  Member "neural tree indexers for text understanding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ordered memory")

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)