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: "swim synthesizing what i mean code search and idiomatic snippet synthesis"
  Abstract: Modern programming frameworks come with large libraries, with diverse applications such as for matching regular expressions, parsing XML files and sending email. Programmers often use search engines such as Google and Bing to learn about existing APIs. In this paper, we describe SWIM, a tool which suggests code snippets given API-related natural language queries such as "generate md5 hash code". The query does not need to contain framework-specific trivia such as the type names or methods of interest.   We translate user queries into the APIs of interest using clickthrough data from the Bing search engine. Then, based on patterns learned from open-source code repositories, we synthesize idiomatic code describing the use of these APIs. We introduce structured call sequences to capture API-usage patterns. Structured call sequences are a generalized form of method call sequences, with if-branches and while-loops to represent conditional and repeated API usage patterns, and are simple to extract and amenable to synthesis.   We evaluated swim with 30 common C# API-related queries received by Bing. For 70% of the queries, the first suggested snippet was a relevant solution, and a relevant solution was present in the top 10 results for all benchmarked queries. The online portion of the workflow is also very responsive, at an average of 1.5 seconds per snippet.

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "trans 3 a transformer based framework for unifying code summarization and code search"):
  with members:
    - "coacor code annotation for code retrieval with reinforcement learning"
    - "ernie enhanced representation through knowledge integration"
    - "a neural model for generating natural language summaries of program subroutines"
    - "characterizing and detecting cuda program bugs"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "aroma code recommendation via structural code search"
    - "deepbillboard systematic physical world testing of autonomous driving systems"
    - "deep reinforcement learning for sequence to sequence models"

Connected through hyperedge HE-3 (induced by source paper "maybe deep neural networks are the best choice for modeling source code"):
  with members:
    - "code completion with neural attention and pointer networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "spell once summon anywhere a two level open vocabulary language model"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "attention is all you need"
    - "context2name a deep learning based approach to infer natural variable names from usage contexts"

Connected through hyperedge HE-4 (induced by source paper "recommending comprehensive solutions for programming tasks by mining crowd knowledge"):
  with members:
    - "strict information retrieval based search term identification for concept location"
    - "effective reformulation of query for code search using crowdsourced knowledge and extra large data analytics"
    - "comment generation for source code state of the art challenges and opportunities"
    - "rack code search in the ide using crowdsourced knowledge"
    - "more accurate recommendations for method level changes"
    - "nlp2code code snippet content assist via natural language tasks"
    - "enriching word vectors with subword information"
    - "rack automatic api recommendation using crowdsourced knowledge"

Connected through hyperedge HE-5 (induced by source paper "recovering variable names for minified code with usage contexts"):
  with members:
    - "parameter free probabilistic api mining across github"
    - "deep api learning"
    - "parameter free probabilistic api mining at github scale"
    - "tbcnn a tree based convolutional neural network for programming language processing"
    - "learning natural coding conventions"

Connected through hyperedge HE-6 (induced by source paper "aroma code recommendation via structural code search"):
  with members:
    - "oreo detection of clones in the twilight zone"
    - "sourcerercc scaling code clone detection to big code"
    - "learning api usages from bytecode a statistical approach"

Connected through hyperedge HE-7 (induced by source paper "code vectors understanding programs through embedded abstracted symbolic traces"):
  with members:
    - "code2vec learning distributed representations of code"
    - "neural code comprehension a learnable representation of code semantics"
    - "a general path based representation for predicting program properties"
    - "path based function embedding and its application to specification mining"
    - "a survey of machine learning for big code and naturalness"
    - "learning to represent programs with graphs"
    - "dynamic neural program embedding for program repair"
    - "learning continuous semantic representations of symbolic expressions"

Connected through hyperedge HE-8 (induced by source paper "staqc a systematically mined question code dataset from stack overflow"):
  with members:
    - "a parallel corpus of python functions and documentation strings for automated code documentation and code generation"
    - "abstract syntax networks for code generation and semantic parsing"
    - "a neural architecture for generating natural language descriptions from source code changes"
    - "a syntactic neural model for general purpose code generation"
    - "nlp2code code snippet content assist via natural language tasks"
    - "from query to usable code an analysis of stack overflow code snippets"
    - "latent predictor networks for code generation"
    - "a convolutional attention network for extreme summarization of source code"

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)