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: "selecting representative examples for program synthesis"
  Abstract: Program synthesis is a class of regression problems where one seeks a solution, in the form of a source-code program, mapping the inputs to their corresponding outputs exactly. Due to its precise and combinatorial nature, program synthesis is commonly formulated as a constraint satisfaction problem, where input-output examples are encoded as constraints and solved with a constraint solver. A key challenge of this formulation is scalability: while constraint solvers work well with a few well-chosen examples, a large set of examples can incur significant overhead in both time and memory. We describe a method to discover a subset of examples that is both small and representative: the subset is constructed iteratively, using a neural network to predict the probability of unchosen examples conditioned on the chosen examples in the subset, and greedily adding the least probable example. We empirically evaluate the representativeness of the subsets constructed by our method, and demonstrate such subsets can significantly improve synthesis time and stability.

Connected through hyperedge HE-1 (induced by source paper "learning transferable graph exploration"):
  with members:
    - "synthetic datasets for neural program synthesis"
    - "dom q net grounded rl on structured language"
    - "learning a sat solver from single bit supervision"
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "learning to search with mctsnets"
    - "meta reinforcement learning of structured exploration strategies"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-2 (induced by source paper "learning neurosymbolic generative models via program synthesis"):
  with members:
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "houdini lifelong learning as program synthesis"
    - "programmatically interpretable reinforcement learning"
    - "verifiable reinforcement learning via policy extraction"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "texturegan controlling deep image synthesis with texture patches"
    - "towards deeper understanding of variational autoencoding models"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "write execute assess program synthesis with a repl"):
  with members:
    - "learning to infer program sketches"
    - "learning to infer and execute 3d shape programs"
    - "neural guided deductive search for real time program synthesis from examples"
    - "automatic program synthesis of long programs with a learned garbage collector"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "program synthesis through reinforcement learning guided tree search"
    - "robustfill neural program learning under noisy i o"
    - "csgnet neural shape parser for constructive solid geometry"

Connected through hyperedge HE-4 (induced by source paper "synthesizing queries via interactive sketching"):
  with members:
    - "active learning of points to specifications"
    - "program synthesis using conflict driven learning"
    - "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"

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.AI(Artificial Intelligence)