human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "multi modal synthesis of regular expressions"):
  Abstract: Despite their usefulness across a wide range of application domains, regular expressions (or regexes for short) have a reputation for being difficult to master. In this paper, we propose a multi-modal synthesis technique for automatically synthesizing regexes from a combination of examples and natural language. Using multiple modalities of the specification is useful in this context because natural language alone is often highly ambiguous, whereas examples in isolation are often not sufficient for conveying the user's intent. Our proposed synthesis algorithm first parses the English description into a so-called hierarchical sketch that is used to guide our programming-by-example (PBE) engine. Since the hierarchical sketch captures key hints embedded in the natural language description, the PBE engine can leverage this information to both prioritize the search as well as make useful deductions for pruning the search space. We have implemented the proposed technique in a tool called REGEL and evaluate it on two different data sets, one curated from StackOverflow and another adopted from prior work. Our evaluation demonstrates the advantages of using multiple modalities of specification and also highlights the impact of key design choices underlying our PBE engine. Finally, a user study involving 20 participants demonstrates that users are more likely to successfully come up with the desired regex using REGEL compared to without using it.

Includes members:
  Member "seq2sql generating structured queries from natural language using reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base")
  Member "nl2bash a corpus and semantic parser for natural language interface to the linux operating system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "clai a platform for ai skills on the command line")
  Member "program synthesis using abstraction refinement":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "resource guided program synthesis")
  Member "program synthesis using conflict driven learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "comparison of syntactic and semantic representations of programs in neural embeddings")
  Member "synthesis of data completion scripts using finite tree automata":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "visualization by example")
  Member "learning a natural language interface with neural programmer":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "plotqa reasoning over scientific plots")
  Member "neural generation of regular expressions from natural language with minimal domain knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "don t paraphrase detect rapid and effective data collection for semantic parsing")
  Member "component based synthesis of table consolidation and transformation tasks from examples":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "counterexample guided neural synthesis")

Question: Please tell me which class the source paper 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.PL(Programming Languages)