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: "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
  Abstract: We present new data and semantic parsing methods for the problem of mapping English sentences to Bash commands (NL2Bash). Our long-term goal is to enable any user to perform operations such as file manipulation, search, and application-specific scripting by simply stating their goals in English. We take a first step in this domain, by providing a new dataset of challenging but commonly used Bash commands and expert-written English descriptions, along with baseline methods to establish performance levels on this task.

Connected through hyperedge HE-1 (induced by source paper "clai a platform for ai skills on the command line"):
  with members:
    - "a bandit approach to posterior dialog orchestration under a budget"
    - "ubuntuworld 1 0 lts a platform for automated problem solving troubleshooting in the ubuntu os"

Connected through hyperedge HE-2 (induced by source paper "spoc search based pseudocode to code"):
  with members:
    - "neural program repair by jointly learning to localize and repair"
    - "frangel component based synthesis with control structures"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "naps natural program synthesis dataset"
    - "mapping language to code in programmatic context"
    - "coarse to fine decoding for neural semantic parsing"
    - "retrieval based neural code generation"
    - "robustfill neural program learning under noisy i o"

Connected through hyperedge HE-3 (induced by source paper "one shot learning for text to sql generation"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "deep contextualized word representations"
    - "typesql knowledge based type aware neural text to sql generation"
    - "coarse to fine decoding for neural semantic parsing"
    - "retrieval based neural code generation"
    - "learning a neural semantic parser from user feedback"

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"
    - "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"
    - "code2seq generating sequences from structured representations of code"

Connected through hyperedge HE-5 (induced by source paper "building an application independent natural language interface"):
  with members:
    - "spatialnli a spatial domain natural language interface to databases using spatial comprehension"
    - "bridging the semantic gap with sql query logs in natural language interfaces to databases"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "transferable natural language interface to structured queries aided by adversarial generation"
    - "an end to end neural natural language interface for databases"
    - "explain yourself a natural language interface for scrutable autonomous robots"
    - "mapping natural language commands to web elements"
    - "unified pragmatic models for generating and following instructions"

Connected through hyperedge HE-6 (induced by source paper "gancoder an automatic natural language to programming language translation approach based on gan"):
  with members:
    - "treegan syntax aware sequence generation with generative adversarial networks"
    - "egocoder intelligent program synthesis with hierarchical sequential neural network model"
    - "a survey on semantic parsing"
    - "abstract syntax networks for code generation and semantic parsing"
    - "a syntactic neural model for general purpose code generation"
    - "language to logical form with neural attention"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "latent predictor networks for code generation"

Connected through hyperedge HE-7 (induced by source paper "multi modal synthesis of regular expressions"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "program synthesis using abstraction refinement"
    - "program synthesis using conflict driven learning"
    - "synthesis of data completion scripts using finite tree automata"
    - "learning a natural language interface with neural programmer"
    - "neural generation of regular expressions from natural language with minimal domain knowledge"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "learning semantic string transformations from examples"

Connected through hyperedge HE-8 (induced by source paper "sketch driven regular expression generation from natural language and examples"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "abstract syntax networks for code generation and semantic parsing"
    - "a syntactic neural model for general purpose code generation"
    - "language to logical form with neural attention"
    - "neural generation of regular expressions from natural language with minimal domain knowledge"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

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)