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: "a deep language model for software code"
  Abstract: Existing language models such as n-grams for software code often fail to capture a long context where dependent code elements scatter far apart. In this paper, we propose a novel approach to build a language model for software code to address this particular issue. Our language model, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term dependencies which occur frequently in software code. Results from our intrinsic evaluation on a corpus of Java projects have demonstrated the effectiveness of our language model. This work contributes to realizing our vision for DeepSoft, an end-to-end, generic deep learning-based framework for modeling software and its development process.

Connected through hyperedge HE-1 (induced by source paper "big code big vocabulary open vocabulary models for source code"):
  with members:
    - "modeling vocabulary for big code machine learning"
    - "on learning meaningful code changes via neural machine translation"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "maybe deep neural networks are the best choice for modeling source code"
    - "the adverse effects of code duplication in machine learning models of code"
    - "frage frequency agnostic word representation"
    - "code completion with neural attention and pointer networks"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "improving the robustness to data inconsistency between training and testing for code completion by hierarchical language model"):
  with members:
    - "program language translation using a grammar driven tree to tree model"
    - "mapping language to code in programmatic context"
    - "code completion with neural attention and pointer networks"
    - "tree to tree neural networks for program translation"
    - "a syntactic neural model for general purpose code generation"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "learning continuous semantic representations of symbolic expressions"
    - "deep api learning"

Connected through hyperedge HE-3 (induced by source paper "multi modal attention network learning for semantic source code retrieval"):
  with members:
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "multimodal dual attention memory for video story question answering"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "multimodal machine learning a survey and taxonomy"
    - "attention is all you need"
    - "a deep reinforced model for abstractive summarization"
    - "dual attention networks for multimodal reasoning and matching"

Connected through hyperedge HE-4 (induced by source paper "maybe deep neural networks are the best choice for modeling source code"):
  with members:
    - "code completion with neural attention and pointer networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "spell once summon anywhere a two level open vocabulary language model"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "attention is all you need"
    - "context2name a deep learning based approach to infer natural variable names from usage contexts"

Connected through hyperedge HE-5 (induced by source paper "improve language modelling for code completion through statement level language model based on statement embedding generated by bilstm"):
  with members:
    - "program language translation using a grammar driven tree to tree model"
    - "mapping language to code in programmatic context"
    - "tree to tree neural networks for program translation"
    - "a syntactic neural model for general purpose code generation"
    - "learning continuous semantic representations of symbolic expressions"
    - "deep api learning"
    - "a convolutional attention network for extreme summarization of source code"
    - "learning program embeddings to propagate feedback on student code"

Connected through hyperedge HE-6 (induced by source paper "generating correctness proofs with neural networks"):
  with members:
    - "learning to prove theorems via interacting with proof assistants"
    - "holist an environment for machine learning of higher order theorem proving"
    - "gamepad a learning environment for theorem proving"
    - "a survey of machine learning for big code and naturalness"
    - "deep network guided proof search"
    - "holstep a machine learning dataset for higher order logic theorem proving"
    - "tactictoe learning to reason with hol4 tactics"
    - "towards proof synthesis guided by neural machine translation for intuitionistic propositional logic"

Connected through hyperedge HE-7 (induced by source paper "neural program synthesis with priority queue training"):
  with members:
    - "robustfill neural program learning under noisy i o"
    - "inferring and executing programs for visual reasoning"
    - "making neural programming architectures generalize via recursion"
    - "a parallel corpus of python functions and documentation strings for automated code documentation and code generation"
    - "learning to reason end to end module networks for visual question answering"
    - "pix2code generating code from a graphical user interface screenshot"
    - "neural sketch learning for conditional program generation"
    - "abstract syntax networks for code generation and semantic parsing"

Connected through hyperedge HE-8 (induced by source paper "improving automatic source code summarization via deep reinforcement learning"):
  with members:
    - "deep reinforcement learning for sequence to sequence models"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "sentence simplification with deep reinforcement learning"
    - "a parallel corpus of python functions and documentation strings for automated code documentation and code generation"
    - "tree to sequence attentional neural machine translation"
    - "deep reinforcement learning for dialogue generation"
    - "from query to usable code an analysis of stack overflow code snippets"
    - "gated graph sequence neural networks"

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.SE(Software Engineering)