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: "neural generation of regular expressions from natural language with minimal domain knowledge"
  Abstract: This paper explores the task of translating natural language queries into regular expressions which embody their meaning. In contrast to prior work, the proposed neural model does not utilize domain-specific crafting, learning to translate directly from a parallel corpus. To fully explore the potential of neural models, we propose a methodology for collecting a large corpus of regular expression, natural language pairs. Our resulting model achieves a performance gain of 19.6% over previous state-of-the-art models.

Connected through hyperedge HE-1 (induced by source paper "don t paraphrase detect rapid and effective data collection for semantic parsing"):
  with members:
    - "learning to learn semantic parsers from natural language supervision"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "end to end open domain question answering with"
    - "practical semantic parsing for spoken language understanding"
    - "building a neural semantic parser from a domain ontology"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "building a conversational agent overnight with dialogue self play"

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 "multi modal synthesis of regular expressions"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "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"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "learning semantic string transformations from examples"

Connected through hyperedge HE-4 (induced by source paper "sketch driven regular expression generation from natural language and examples"):
  with members:
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "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"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "mathqa towards interpretable math word problem solving with operation based formalisms"):
  with members:
    - "a meaning based statistical english math word problem solver"
    - "mapping to declarative knowledge for word problem solving"
    - "learning structured natural language representations for semantic parsing"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "annotating derivations a new evaluation strategy and dataset for algebra word problems"
    - "adam a method for stochastic optimization"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

Connected through hyperedge HE-6 (induced by source paper "mapping language to code in programmatic context"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "learning a neural semantic parser from user feedback"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "deepcoder learning to write programs"
    - "latent predictor networks for code generation"
    - "deep api learning"
    - "a convolutional attention network for extreme summarization of source code"

Connected through hyperedge HE-7 (induced by source paper "learning to mine aligned code and natural language pairs from stack overflow"):
  with members:
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "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 syntactic neural model for general purpose code generation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "from query to usable code an analysis of stack overflow code snippets"

Connected through hyperedge HE-8 (induced by source paper "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "learning a neural semantic parser from user feedback"
    - "a syntactic neural model for general purpose code generation"
    - "language to logical form with neural attention"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "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)