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: "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.

Connected through hyperedge HE-1 (induced by source paper "deep transfer learning for source code modeling"):
  with members:
    - "code2vec learning distributed representations of code"
    - "autofolding for source code summarization"
    - "attention is all you need"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "collective intelligence for smarter api recommendations in python"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "deep api learning"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-2 (induced by source paper "codegru context aware deep learning with gated recurrent unit for source code modeling"):
  with members:
    - "abstract syntax networks for code generation and semantic parsing"
    - "collective intelligence for smarter api recommendations in python"
    - "deep api learning"
    - "a convolutional attention network for extreme summarization of source code"
    - "adam a method for stochastic optimization"
    - "word2vec parameter learning explained"
    - "mining idioms from source code"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)