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: "synthesis of data completion scripts using finite tree automata"
  Abstract: In application domains that store data in a tabular format, a common task is to fill the values of some cells using values stored in other cells. For instance, such data completion tasks arise in the context of missing value imputation in data science and derived data computation in spreadsheets and relational databases. Unfortunately, end-users and data scientists typically struggle with many data completion tasks that require non-trivial programming expertise. This paper presents a synthesis technique for automating data completion tasks using programming-by-example (PBE) and a very lightweight sketching approach. Given a formula sketch (e.g., AVG($?_1$, $?_2$)) and a few input-output examples for each hole, our technique synthesizes a program to automate the desired data completion task. Towards this goal, we propose a domain-specific language (DSL) that combines spatial and relational reasoning over tabular data and a novel synthesis algorithm that can generate DSL programs that are consistent with the input-output examples. The key technical novelty of our approach is a new version space learning algorithm that is based on finite tree automata (FTA). The use of FTAs in the learning algorithm leads to a more compact representation that allows more sharing between programs that are consistent with the examples. We have implemented the proposed approach in a tool called DACE and evaluate it on 84 benchmarks taken from online help forums. We also illustrate the advantages of our approach by comparing our technique against two existing synthesizers, namely PROSE and SKETCH.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "visualization by example"):
  with members:
    - "synthesizing bijective lenses"
    - "program synthesis using abstraction refinement"
    - "program synthesis using conflict driven learning"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "program synthesis from polymorphic refinement types"

Connected through hyperedge HE-3 (induced by source paper "learning abstractions for program synthesis"):
  with members:
    - "program synthesis using abstraction refinement"
    - "synthesizing imperative programs from examples guided by static analysis"
    - "automated migration of hierarchical data to relational tables using programming by example"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "program synthesis from polymorphic refinement types"
    - "sat based methods for circuit synthesis"
    - "interpolant based transition relation approximation"

Connected through hyperedge HE-4 (induced by source paper "relational program synthesis"):
  with members:
    - "program synthesis using abstraction refinement"
    - "synthesizing imperative programs from examples guided by static analysis"
    - "automated migration of hierarchical data to relational tables using programming by example"
    - "verifying equivalence of database driven applications"
    - "program synthesis from polymorphic refinement types"

Connected through hyperedge HE-5 (induced by source paper "program synthesis using conflict driven learning"):
  with members:
    - "program synthesis using abstraction refinement"
    - "deepcoder learning to write programs"
    - "learning a natural language interface with neural programmer"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "neural programmer inducing latent programs with gradient descent"
    - "program synthesis from polymorphic refinement types"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-6 (induced by source paper "program synthesis using abstraction refinement"):
  with members:
    - "robustfill neural program learning under noisy i o"
    - "sygus comp 2016 results and analysis"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "program synthesis from polymorphic refinement types"
    - "using program synthesis for social recommendations"

Connected through hyperedge HE-7 (induced by source paper "automated migration of hierarchical data to relational tables using programming by example"):
  with members:
    - "program synthesis using abstraction refinement"
    - "deepcoder learning to write programs"
    - "component based synthesis of table consolidation and transformation tasks from examples"

Connected through hyperedge HE-8 (induced by source paper "webrelate integrating web data with spreadsheets using examples"):
  with members:
    - "robustfill neural program learning under noisy i o"
    - "program synthesis using abstraction refinement"
    - "learning semantic string transformations from examples"

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.PL(Programming Languages)