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 "improve language modelling for code completion through statement level language model based on statement embedding generated by bilstm"):
  Abstract: Language models such as RNN, LSTM or other variants have been widely used as generative models in natural language processing. In last few years, taking source code as natural languages, parsing source code into a token sequence and using a language model such as LSTM to train that sequence are state-of-art methods to get a generative model for solving the problem of code completion. However, for source code with hundreds of statements, traditional LSTM model or attention-based LSTM model failed to capture the long term dependency of source code. In this paper, we propose a novel statement-level language model (SLM) which uses BiLSTM to generate the embedding for each statement. The standard LSTM is adopted in SLM to iterate and accumulate the embedding of each statement in context to help predict next code. The statement level attention mechanism is also adopted in the model. The proposed model SLM is aimed at token level code completion. The experiments on inner-project and cross-project data sets indicate that the newly proposed statement-level language model with attention mechanism (SLM) outperforms all other state-of-art models in token level code completion task.

Includes members:
  Member "program language translation using a grammar driven tree to tree model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving the robustness to data inconsistency between training and testing for code completion by hierarchical language model")
  Member "mapping language to code in programmatic context":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "structural language models of code")
  Member "tree to tree neural networks for program translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "does syntax need to grow on trees sources of hierarchical inductive bias in sequence to sequence networks")
  Member "a syntactic neural model for general purpose code generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploring neural models for parsing natural language into first order logic")
  Member "learning continuous semantic representations of symbolic expressions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relational neural machines")
  Member "a deep language model for software code":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "deep api learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "a convolutional attention network for extreme summarization of source code":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "big code big vocabulary open vocabulary models for source code")

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.SE(Software Engineering)