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: "program synthesis using conflict driven learning"
  Abstract: We propose a new conflict-driven program synthesis technique that is capable of learning from past mistakes. Given a spurious program that violates the desired specification, our synthesis algorithm identifies the root cause of the conflict and learns new lemmas that can prevent similar mistakes in the future. Specifically, we introduce the notion of equivalence modulo conflict and show how this idea can be used to learn useful lemmas that allow the synthesizer to prune large parts of the search space. We have implemented a general-purpose CDCL-style program synthesizer called Neo and evaluate it in two different application domains, namely data wrangling in R and functional programming over lists. Our experiments demonstrate the substantial benefits of conflict-driven learning and show that Neo outperforms two state-of-the-art synthesis tools, Morpheus and Deepcoder, that target these respective domains.

Connected through hyperedge HE-1 (induced by source paper "comparison of syntactic and semantic representations of programs in neural embeddings"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "code2vec learning distributed representations of code"
    - "naps natural program synthesis dataset"
    - "neural guided deductive search for real time program synthesis from examples"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "neural program search solving programming tasks from description and examples"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "neural guided constraint logic programming for program synthesis"

Connected through hyperedge HE-2 (induced by source paper "resource guided program synthesis"):
  with members:
    - "verifying and synthesizing constant resource implementations with types"
    - "program synthesis using abstraction refinement"
    - "towards automatic resource bound analysis for ocaml"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "synthesis of recursive adt transformations from reusable templates"
    - "program synthesis from polymorphic refinement types"
    - "denotational cost semantics for functional languages with inductive types"
    - "from non preemptive to preemptive scheduling using synchronization synthesis"

Connected through hyperedge HE-3 (induced by source paper "synthesizing queries via interactive sketching"):
  with members:
    - "selecting representative examples for program synthesis"
    - "active learning of points to specifications"
    - "interactive program synthesis"
    - "interactive bayesian hierarchical clustering"
    - "deepcoder learning to write programs"
    - "synthesizing program input grammars"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "program synthesis from polymorphic refinement types"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "synfuzz efficient concolic execution via branch condition synthesis"):
  with members:
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "evaluating fuzz testing"
    - "angora efficient fuzzing by principled search"
    - "slowfuzz automated domain independent detection of algorithmic complexity vulnerabilities"
    - "not all bytes are equal neural byte sieve for fuzzing"
    - "learn fuzz machine learning for input fuzzing"
    - "deepcoder learning to write programs"
    - "synthesizing program input grammars"

Connected through hyperedge HE-6 (induced by source paper "synthesizing database programs for schema refactoring"):
  with members:
    - "relational program synthesis"
    - "program synthesis using abstraction refinement"
    - "verifying equivalence of database driven applications"
    - "deepcoder learning to write programs"
    - "hottsql proving query rewrites with univalent sql semantics"
    - "on the automated verification of web applications with embedded sql"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "program synthesis from polymorphic refinement types"

Connected through hyperedge HE-7 (induced by source paper "an evolutionary framework for automatic and guided discovery of algorithms"):
  with members:
    - "neural guided deductive search for real time program synthesis from examples"
    - "deepcoder learning to write programs"
    - "programmatic and direct manipulation together at last"
    - "semi automated svg programming via direct manipulation"
    - "program synthesis from polymorphic refinement types"
    - "automated feedback generation for introductory programming assignments"

Connected through hyperedge HE-8 (induced by source paper "visualization by example"):
  with members:
    - "synthesizing bijective lenses"
    - "program synthesis using abstraction refinement"
    - "synthesis of data completion scripts using finite tree automata"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "program synthesis from polymorphic refinement types"

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)