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 "codit code editing with tree based neuralmachine translation"):
  Abstract: The way developers edit day-to-day code tends to be repetitive, often using existing code elements. Many researchers have tried to automate repetitive code changes by learning from specific change templates which are applied to limited scope. The advancement of Neural Machine Translation (NMT) and the availability of vast open-source evolutionary data opens up the possibility of automatically learning those templates from the wild. However, unlike natural languages, for which NMT techniques were originally devised, source code and its changes have certain properties. For instance, compared to natural language, source code vocabulary can be significantly larger. Further, good changes in code do not break its syntactic structure. Thus, deploying state-of-the-art NMT models without adapting the methods to the source code domain yields sub-optimal results. To this end, we propose a novel Tree based NMT system to model source code changes and learn code change patterns from the wild. We realize our model with a change suggestion engine: CODIT and train the model with more than 30k real-world changes and evaluate it on 6k patches. Our evaluation shows the effectiveness of CODIT in learning and suggesting patches.CODIT also shows promise generating bug fix patches.

Includes members:
  Member "on learning meaningful code changes via neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "sequencer sequence to sequence learning for end to end program repair":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "code2vec learning distributed representations of code":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial robustness for code")
  Member "learning to represent edits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "structural language models of code")
  Member "building language models for text with named entities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "latent relation language models")
  Member "tree to tree neural networks for program translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "does syntax need to grow on trees sources of hierarchical inductive bias in sequence to sequence networks")
  Member "opennmt open source toolkit for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training")
  Member "improved neural machine translation with a syntax aware encoder and decoder":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")

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)