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: "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.

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"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "searching a database of source codes using contextualized code search"):
  with members:
    - "multi modal attention network learning for semantic source code retrieval"
    - "when deep learning met code search"
    - "mapping language to code in programmatic context"
    - "tree2tree neural translation model for learning source code changes"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "top down tree structured decoding with syntactic connections for neural machine translation and parsing"
    - "robustfill neural program learning under noisy i o"
    - "variational deep semantic hashing for text documents"

Connected through hyperedge HE-3 (induced by source paper "on learning meaningful assert statements for unit test cases"):
  with members:
    - "a neural model for generating natural language summaries of program subroutines"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "learning how to mutate source code from bug fixes"
    - "learning to generate corrective patches using neural machine translation"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "six challenges for neural machine translation"
    - "deep api learning"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "is your quantum program bug free"):
  with members:
    - "on testing quantum programs"

Connected through hyperedge HE-5 (induced by source paper "modeling vocabulary for big code machine learning"):
  with members:
    - "maybe deep neural networks are the best choice for modeling source code"
    - "code2vec learning distributed representations of code"
    - "neural code comprehension a learnable representation of code semantics"
    - "a general path based representation for predicting program properties"
    - "the adverse effects of code duplication in machine learning models of code"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep rnns encode soft hierarchical syntax"

Connected through hyperedge HE-6 (induced by source paper "software engineering meets deep learning a literature review"):
  with members:
    - "feature maps a comprehensible software representation for design pattern detection"
    - "on using machine learning to identify knowledge in api reference documentation"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "when deep learning met code search"
    - "a comprehensive study on deep learning bug characteristics"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "csindexbr exploring the brazilian scientific production in computer science"

Connected through hyperedge HE-7 (induced by source paper "sequencer sequence to sequence learning for end to end program repair"):
  with members:
    - "maybe deep neural networks are the best choice for modeling source code"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "the codrep machine learning on source code competition"
    - "an empirical study of fault localization families and their combinations"
    - "automatic software repair a bibliography"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "learning to generate corrective patches using neural machine translation"
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"

Connected through hyperedge HE-8 (induced by source paper "when deep learning met code search"):
  with members:
    - "aroma code recommendation via structural code search"
    - "code2vec learning distributed representations of code"
    - "interpretable lstms for whole brain neuroimaging analyses"
    - "analyzing neuroimaging data through recurrent deep learning models"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "robust text to sql generation with execution guided decoding"
    - "a deep tree based model for software defect prediction"
    - "semantically enhanced software traceability using deep learning techniques"

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)