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: "mining idioms from source code"
  Abstract: We present the first method for automatically mining code idioms from a corpus of previously written, idiomatic software projects. We take the view that a code idiom is a syntactic fragment that recurs across projects and has a single semantic role. Idioms may have metavariables, such as the body of a for loop. Modern IDEs commonly provide facilities for manually defining idioms and inserting them on demand, but this does not help programmers to write idiomatic code in languages or using libraries with which they are unfamiliar. We present HAGGIS, a system for mining code idioms that builds on recent advanced techniques from statistical natural language processing, namely, nonparametric Bayesian probabilistic tree substitution grammars. We apply HAGGIS to several of the most popular open source projects from GitHub. We present a wide range of evidence that the resulting idioms are semantically meaningful, demonstrating that they do indeed recur across software projects and that they occur more frequently in illustrative code examples collected from a Q&A site. Manual examination of the most common idioms indicate that they describe important program concepts, including object creation, exception handling, and resource management.

Connected through hyperedge HE-1 (induced by source paper "program synthesis and semantic parsing with learned code idioms"):
  with members:
    - "encoding database schemas with relation aware self attention for text to sql parsers"
    - "learning programmatic idioms for scalable semantic parsing"
    - "a grammar based structural cnn decoder for code generation"
    - "self attention with relative position representations"
    - "generative code modeling with graphs"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "coarse to fine decoding for neural semantic parsing"
    - "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task"

Connected through hyperedge HE-2 (induced by source paper "juice a large scale distantly supervised dataset for open domain context based code generation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "learning to map context dependent sentences to executable formal queries"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "mapping language to code in programmatic context"
    - "learning to mine aligned code and natural language pairs from stack overflow"
    - "attention is all you need"
    - "learning a neural semantic parser from user feedback"
    - "abstract syntax networks for code generation and semantic parsing"

Connected through hyperedge HE-3 (induced by source paper "learning programmatic idioms for scalable semantic parsing"):
  with members:
    - "learning to infer program sketches"
    - "program synthesis and semantic parsing with learned code idioms"
    - "improving text to sql evaluation methodology"
    - "mapping language to code in programmatic context"
    - "coarse to fine decoding for neural semantic parsing"
    - "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task"
    - "retrieval based neural code generation"
    - "neural sketch learning for conditional program generation"

Connected through hyperedge HE-4 (induced by source paper "codegru context aware deep learning with gated recurrent unit for source code modeling"):
  with members:
    - "an exploration of dropout with rnns for natural language inference"
    - "abstract syntax networks for code generation and semantic parsing"
    - "collective intelligence for smarter api recommendations in python"
    - "deep api learning"
    - "a convolutional attention network for extreme summarization of source code"
    - "adam a method for stochastic optimization"
    - "word2vec parameter learning explained"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-5 (induced by source paper "do people prefer natural code"):
  with members:
    - "automatically generating documentation for lambda expressions in java"
    - "replication can improve prior results a github study of pull request acceptance"
    - "the adverse effects of code duplication in machine learning models of code"
    - "categorizing the content of github readme files"
    - "studying the difference between natural and programming language corpora"
    - "attention is all you need"
    - "a survey of machine learning for big code and naturalness"
    - "on the naturalness of buggy code"

Connected through hyperedge HE-6 (induced by source paper "are my invariants valid a learning approach"):
  with members:
    - "generative code modeling with graphs"
    - "learning to represent programs with graphs"
    - "dynamic neural program embedding for program repair"
    - "gated graph sequence neural networks"
    - "tensorflow a system for large scale machine learning"
    - "on the naturalness of buggy code"
    - "adam a method for stochastic optimization"
    - "learning natural coding conventions"

Connected through hyperedge HE-7 (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"
    - "tree2tree neural translation model for learning source code changes"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "code2vec learning distributed representations of code"):
  with members:
    - "a general path based representation for predicting program properties"
    - "neural attribute machines for program generation"
    - "attention is all you need"
    - "zero shot relation extraction via reading comprehension"
    - "a survey of machine learning for big code and naturalness"
    - "neural sketch learning for conditional program generation"
    - "data driven program completion"
    - "learning to represent programs with graphs"

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)