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: "tree2tree neural translation model for learning source code changes"
  Abstract: The way developers edit day-to-day code tend to be repetitive and often use existing code elements. Many researchers tried to automate this tedious task of code changes by learning from specific change templates and applied to limited scope. The advancement of Neural Machine Translation (NMT) and the availability of the vast open source software evolutionary data open up a new possibility of automatically learning those templates from the wild. However, unlike natural languages, for which NMT techniques were originally designed, source code and the changes have certain properties. For instance, compared to natural language source code vocabulary can be virtually infinite. Further, any good change in code should not break its syntactic structure. Thus, deploying state-of-the-art NMT models without domain adaptation may poorly serve the purpose. To this end, in this work, we propose a novel Tree2Tree Neural Machine Translation system to model source code changes and learn code change patterns from the wild. We realize our model with a change suggestion engine: CODIT. We train the model with more than 30k real-world changes and evaluate it with 6k patches. Our evaluation shows the effectiveness of CODIT in learning and suggesting abstract change templates. CODIT also shows promise in suggesting concrete patches and generating bug fixes.

Connected through hyperedge HE-1 (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"
    - "on learning meaningful code changes via neural machine translation"
    - "when deep learning met code search"
    - "mapping language to code in programmatic context"
    - "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-2 (induced by source paper "in nomine function naming functions in stripped binaries with neural networks"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "dire a neural approach to decompiled identifier naming"
    - "neural reverse engineering of stripped binaries"
    - "universal language model fine tuning for text classification"
    - "a deep tree based model for software defect prediction"
    - "code2seq generating sequences from structured representations of code"
    - "attention is all you need"
    - "context2name a deep learning based approach to infer natural variable names from usage contexts"

Connected through hyperedge HE-3 (induced by source paper "encore ensemble learning using convolution neural machine translation for automatic program repair"):
  with members:
    - "a comprehensive study of automatic program repair on the quixbugs benchmark"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "code2vec learning distributed representations of code"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "fixminer mining relevant fix patterns for automated program repair"
    - "you cannot fix what you cannot find an investigation of fault localization bias in benchmarking automated program repair systems"

Connected through hyperedge HE-4 (induced by source paper "tree transformer a transformer based method for correction of tree structured data"):
  with members:
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "automatic software repair a bibliography"
    - "learning to repair software vulnerabilities with generative adversarial networks"
    - "tree to tree neural networks for program translation"
    - "semantic code repair using neuro symbolic transformation networks"
    - "a nested attention neural hybrid model for grammatical error correction"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "sequencer sequence to sequence learning for end to end program repair"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "learning lenient parsing typing via indirect supervision"):
  with members:
    - "modeling vocabulary for big code machine learning"
    - "on learning meaningful code changes via neural machine translation"
    - "maybe deep neural networks are the best choice for modeling source code"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "deep reinforcement learning for programming language correction"
    - "codit code editing with tree based neuralmachine translation"
    - "sgdr stochastic gradient descent with restarts"
    - "automated correction for syntax errors in programming assignments using recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "can neural networks learn symbolic rewriting"):
  with members:
    - "first experiments with neural translation of informal to formal mathematics"
    - "can neural networks understand logical entailment"
    - "attention is all you need"
    - "developing corpus based translation methods between informal and formal mathematics project description"

Connected through hyperedge HE-8 (induced by source paper "learning to generate corrective patches using neural machine translation"):
  with members:
    - "harnessing evolution for multi hunk program repair"
    - "on learning meaningful code changes via neural machine translation"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "how different are different diff algorithms in git use histogram for code changes"
    - "automatic software repair a bibliography"
    - "an empirical study on learning bug fixing patches in the wild via neural machine translation"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "nopol automatic repair of conditional statement bugs in java programs"

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)