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 "on learning meaningful code changes via neural machine translation"):
  Abstract: Recent years have seen the rise of Deep Learning (DL) techniques applied to source code. Researchers have exploited DL to automate several development and maintenance tasks, such as writing commit messages, generating comments and detecting vulnerabilities among others. One of the long lasting dreams of applying DL to source code is the possibility to automate non-trivial coding activities. While some steps in this direction have been taken (e.g., learning how to fix bugs), there is still a glaring lack of empirical evidence on the types of code changes that can be learned and automatically applied by DL. Our goal is to make this first important step by quantitatively and qualitatively investigating the ability of a Neural Machine Translation (NMT) model to learn how to automatically apply code changes implemented by developers during pull requests. We train and experiment with the NMT model on a set of 236k pairs of code components before and after the implementation of the changes provided in the pull requests. We show that, when applied in a narrow enough context (i.e., small/medium-sized pairs of methods before/after the pull request changes), NMT can automatically replicate the changes implemented by developers during pull requests in up to 36% of the cases. Moreover, our qualitative analysis shows that the model is capable of learning and replicating a wide variety of meaningful code changes, especially refactorings and bug-fixing activities. Our results pave the way for novel research in the area of DL on code, such as the automatic learning and applications of refactoring.

Includes members:
  Member "machine learning based prototyping of graphical user interfaces for mobile apps":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "guicomp a gui design assistant with real time multi faceted feedback")
  Member "semantically enhanced software traceability using deep learning techniques":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "software engineering meets deep learning a literature review")
  Member "automatically generating commit messages from diffs using neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "automating app review response generation")
  Member "massive exploration of neural machine translation architectures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta embeddings based on self attention")
  Member "learn fuzz machine learning for input fuzzing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing")
  Member "deepsoft a vision for a deep model of software":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "v fuzz vulnerability oriented evolutionary fuzzing")
  Member "a deep learning model for estimating story points":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "software engineering meets deep learning a literature review")
  Member "vocabulary manipulation for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sequence generation from both sides to the middle")

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)