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: "a parallel corpus of python functions and documentation strings for automated code documentation and code generation"
  Abstract: Automated documentation of programming source code and automated code generation from natural language are challenging tasks of both practical and scientific interest. Progress in these areas has been limited by the low availability of parallel corpora of code and natural language descriptions, which tend to be small and constrained to specific domains. #R##N#In this work we introduce a large and diverse parallel corpus of a hundred thousands Python functions with their documentation strings ("docstrings") generated by scraping open source repositories on GitHub. We describe baseline results for the code documentation and code generation tasks obtained by neural machine translation. We also experiment with data augmentation techniques to further increase the amount of training data. #R##N#We release our datasets and processing scripts in order to stimulate research in these areas.

Connected through hyperedge HE-1 (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-2 (induced by source paper "pre trained contextual embedding of source code"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "neural program repair by jointly learning to localize and repair"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "when deep learning met code search"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep learning to detect redundant method comments"
    - "code2vec learning distributed representations of code"
    - "self attention with relative position representations"

Connected through hyperedge HE-3 (induced by source paper "a neural model for generating natural language summaries of program subroutines"):
  with members:
    - "deep learning to detect redundant method comments"
    - "learning to mine aligned code and natural language pairs from stack overflow"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "attention is all you need"
    - "the code2text challenge text generation in source libraries"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "a survey of machine learning for big code and naturalness"
    - "a neural architecture for generating natural language descriptions from source code changes"

Connected through hyperedge HE-4 (induced by source paper "codesearchnet challenge evaluating the state of semantic code search"):
  with members:
    - "coacor code annotation for code retrieval with reinforcement learning"
    - "when deep learning met code search"
    - "structured neural summarization"
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "the adverse effects of code duplication in machine learning models of code"
    - "mapping language to code in programmatic context"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "staqc a systematically mined question code dataset from stack overflow"

Connected through hyperedge HE-5 (induced by source paper "code generation as a dual task of code summarization"):
  with members:
    - "a grammar based structural cnn decoder for code generation"
    - "dual ask answer network for machine reading comprehension"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "dual supervised learning"
    - "abstract syntax networks for code generation and semantic parsing"
    - "question answering and question generation as dual tasks"
    - "a syntactic neural model for general purpose code generation"
    - "tree to sequence attentional neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "a survey on semantic parsing"):
  with members:
    - "learning programmatic idioms for scalable semantic parsing"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "mapping language to code in programmatic context"
    - "semantic parsing with syntax and table aware sql generation"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "improving a neural semantic parser by counterfactual learning from human bandit feedback"

Connected through hyperedge HE-7 (induced by source paper "neural program synthesis with priority queue training"):
  with members:
    - "robustfill neural program learning under noisy i o"
    - "inferring and executing programs for visual reasoning"
    - "making neural programming architectures generalize via recursion"
    - "learning to reason end to end module networks for visual question answering"
    - "pix2code generating code from a graphical user interface screenshot"
    - "neural sketch learning for conditional program generation"
    - "abstract syntax networks for code generation and semantic parsing"
    - "ai programmer autonomously creating software programs using genetic algorithms"

Connected through hyperedge HE-8 (induced by source paper "improving automatic source code summarization via deep reinforcement learning"):
  with members:
    - "deep reinforcement learning for sequence to sequence models"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "sentence simplification with deep reinforcement learning"
    - "tree to sequence attentional neural machine translation"
    - "deep reinforcement learning for dialogue generation"
    - "from query to usable code an analysis of stack overflow code snippets"
    - "gated graph sequence neural networks"
    - "re evaluating automatic metrics for image captioning"

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.CL(Computation and Language)