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 sketch learning for conditional program generation"
  Abstract: We study the problem of generating source code in a strongly typed, Java-like programming language, given a label (for example a set of API calls or types) carrying a small amount of information about the code that is desired. The generated programs are expected to respect a "realistic" relationship between programs and labels, as exemplified by a corpus of labeled programs available during training. #R##N#Two challenges in such conditional program generation are that the generated programs must satisfy a rich set of syntactic and semantic constraints, and that source code contains many low-level features that impede learning. We address these problems by training a neural generator not on code but on program sketches, or models of program syntax that abstract out names and operations that do not generalize across programs. During generation, we infer a posterior distribution over sketches, then concretize samples from this distribution into type-safe programs using combinatorial techniques. We implement our ideas in a system for generating API-heavy Java code, and show that it can often predict the entire body of a method given just a few API calls or data types that appear in the method.

Connected through hyperedge HE-1 (induced by source paper "learning compositional rules via neural program synthesis"):
  with members:
    - "learning to infer program sketches"
    - "learning to prove theorems via interacting with proof assistants"
    - "human few shot learning of compositional instructions"
    - "compositional generalization through meta sequence to sequence learning"
    - "write execute assess program synthesis with a repl"
    - "good enough compositional data augmentation"
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "automatic program synthesis of long programs with a learned garbage collector"

Connected through hyperedge HE-2 (induced by source paper "adversarial examples for models of code"):
  with members:
    - "combating adversarial misspellings with robust word recognition"
    - "gnn film graph neural networks with feature wise linear modulation"
    - "when deep learning met code search"
    - "getafix learning to fix bugs automatically"
    - "structural language models for any code generation"
    - "code2vec learning distributed representations of code"
    - "targeted adversarial examples for black box audio systems"
    - "a general path based representation for predicting program properties"

Connected through hyperedge HE-3 (induced by source paper "encore ensemble learning using convolution neural machine translation for automatic program repair"):
  with members:
    - "a comprehensive study of automatic program repair on the quixbugs benchmark"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "code2vec learning distributed representations of code"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "fixminer mining relevant fix patterns for automated program repair"
    - "tree2tree neural translation model for learning source code changes"

Connected through hyperedge HE-4 (induced by source paper "imitation projected programmatic reinforcement learning"):
  with members:
    - "batch policy learning under constraints"
    - "control regularization for reduced variance reinforcement learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "houdini lifelong learning as program synthesis"
    - "safe exploration in continuous action spaces"
    - "predictor corrector policy optimization"
    - "programmatically interpretable reinforcement learning"
    - "accelerating imitation learning with predictive models"

Connected through hyperedge HE-5 (induced by source paper "neural reverse engineering of stripped binaries"):
  with members:
    - "structured neural summarization"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "the adverse effects of code duplication in machine learning models of code"
    - "mapping language to code in programmatic context"
    - "generative code modeling with graphs"
    - "tree to tree neural networks for program translation"
    - "code2seq generating sequences from structured representations of code"

Connected through hyperedge HE-6 (induced by source paper "program synthesis and semantic parsing with learned code idioms"):
  with members:
    - "encoding database schemas with relation aware self attention for text to sql parsers"
    - "learning programmatic idioms for scalable semantic parsing"
    - "a grammar based structural cnn decoder for code generation"
    - "self attention with relative position representations"
    - "generative code modeling with graphs"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "coarse to fine decoding for neural semantic parsing"
    - "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task"

Connected through hyperedge HE-7 (induced by source paper "neurally guided structure inference"):
  with members:
    - "learning to infer and execute 3d shape programs"
    - "automatic program synthesis of long programs with a learned garbage collector"
    - "robust text to sql generation with execution guided decoding"
    - "tree to tree neural networks for program translation"
    - "neural guided constraint logic programming for program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "making neural programming architectures generalize via recursion"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-8 (induced by source paper "a literature study of embeddings on source code"):
  with members:
    - "import2vec learning embeddings for software libraries"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "semantic embeddings for program behavior patterns"
    - "code2vec learning distributed representations of code"
    - "the remarkable role of similarity in redundancy based program repair"
    - "learning to represent edits"
    - "a cross architecture instruction embedding model for natural language processing inspired binary code analysis"
    - "path based function embedding and its application to specification mining"

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)