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: "maybe deep neural networks are the best choice for modeling source code"
  Abstract: Statistical language modeling techniques have successfully been applied to source code, yielding a variety of new software development tools, such as tools for code suggestion and improving readability. A major issue with these techniques is that code introduces new vocabulary at a far higher rate than natural language, as new identifier names proliferate. But traditional language models limit the vocabulary to a fixed set of common words. For code, this strong assumption has been shown to have a significant negative effect on predictive performance. But the open vocabulary version of the neural network language models for code have not been introduced in the literature. We present a new open-vocabulary neural language model for code that is not limited to a fixed vocabulary of identifier names. We employ a segmentation into subword units, subsequences of tokens chosen based on a compression criterion, following previous work in machine translation. Our network achieves best in class performance, outperforming even the state-of-the-art methods of Hellendoorn and Devanbu that are designed specifically to model code. Furthermore, we present a simple method for dynamically adapting the model to a new test project, resulting in increased performance. We showcase our methodology on code corpora in three different languages of over a billion tokens each, hundreds of times larger than in previous work. To our knowledge, this is the largest neural language model for code that has been reported.

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"
    - "on learning meaningful code changes via neural machine translation"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "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 "modeling programs hierarchically with stack augmented lstm"):
  with members:
    - "open vocabulary learning on source code with a graph structured cache"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "code completion with neural attention and pointer networks"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "attention is all you need"
    - "learning to represent programs with graphs"
    - "learning python code suggestion with a sparse pointer network"

Connected through hyperedge HE-3 (induced by source paper "modeling vocabulary for big code machine learning"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "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-4 (induced by source paper "learning to fix build errors with graph2diff neural networks"):
  with members:
    - "harnessing evolution for multi hunk program repair"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "neural program repair by jointly learning to localize and repair"
    - "getafix learning to fix bugs automatically"
    - "a comprehensive survey on graph neural networks"
    - "generative code modeling with graphs"
    - "graph to sequence learning using gated graph neural networks"
    - "neural networks for modeling source code edits"

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"
    - "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-6 (induced by source paper "typewriter neural type prediction with search based validation"):
  with members:
    - "on learning meaningful assert statements for unit test cases"
    - "big code big vocabulary open vocabulary models for source code"
    - "modeling vocabulary for big code machine learning"
    - "on learning meaningful code changes via neural machine translation"
    - "getafix learning to fix bugs automatically"
    - "learning to represent edits"
    - "neural networks for modeling source code edits"
    - "learning to repair software vulnerabilities with generative adversarial networks"

Connected through hyperedge HE-7 (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"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "deep reinforcement learning for programming language correction"
    - "tree2tree neural translation model for learning source code changes"
    - "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-8 (induced by source paper "using sequence to sequence learning for repairing c vulnerabilities"):
  with members:
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "learning to repair software vulnerabilities with generative adversarial networks"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "mining software repair models for reasoning on the search space of automated program fixing"
    - "neural machine translation of rare words with subword units"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "sequence to sequence learning with neural networks"

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)