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 attribute machines for program generation"
  Abstract: Recurrent neural networks have achieved remarkable success at generating sequences with complex structures, thanks to advances that include richer embeddings of input and cures for vanishing gradients. Trained only on sequences from a known grammar, though, they can still struggle to learn rules and constraints of the grammar. #R##N#Neural Attribute Machines (NAMs) are equipped with a logical machine that represents the underlying grammar, which is used to teach the constraints to the neural machine by (i) augmenting the input sequence, and (ii) optimizing a custom loss function. Unlike traditional RNNs, NAMs are exposed to the grammar, as well as samples from the language of the grammar. During generation, NAMs make significantly fewer violations of the constraints of the underlying grammar than RNNs trained only on samples from the language of the grammar.

Connected through hyperedge HE-1 (induced by source paper "structural language models of code"):
  with members:
    - "learning neurosymbolic generative models via program synthesis"
    - "learning to infer program sketches"
    - "learning programmatic idioms for scalable semantic parsing"
    - "write execute assess program synthesis with a repl"
    - "structured neural summarization"
    - "open vocabulary learning on source code with a graph structured cache"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"

Connected through hyperedge HE-2 (induced by source paper "structural language models for any code generation"):
  with members:
    - "structural language models of code"
    - "learning neurosymbolic generative models via program synthesis"
    - "learning to infer program sketches"
    - "learning programmatic idioms for scalable semantic parsing"
    - "gnn film graph neural networks with feature wise linear modulation"
    - "write execute assess program synthesis with a repl"
    - "structured neural summarization"
    - "open vocabulary learning on source code with a graph structured cache"

Connected through hyperedge HE-3 (induced by source paper "towards neural decompilation"):
  with members:
    - "tree to tree neural networks for program translation"
    - "codesum translate program language to natural language"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "six challenges for neural machine translation"
    - "a neural architecture for generating natural language descriptions from source code changes"
    - "a convolutional attention network for extreme summarization of source code"
    - "tensorflow a system for large scale machine learning"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-4 (induced by source paper "code2vec learning distributed representations of code"):
  with members:
    - "a general path based representation for predicting program properties"
    - "attention is all you need"
    - "zero shot relation extraction via reading comprehension"
    - "a survey of machine learning for big code and naturalness"
    - "neural sketch learning for conditional program generation"
    - "data driven program completion"
    - "learning to represent programs with graphs"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-5 (induced by source paper "open vocabulary learning on source code with a graph structured cache"):
  with members:
    - "neural program repair by jointly learning to localize and repair"
    - "a general path based representation for predicting program properties"
    - "generative code modeling with graphs"
    - "code completion with neural attention and pointer networks"
    - "relational inductive biases deep learning and graph networks"
    - "tree to tree neural networks for program translation"
    - "graph memory networks for molecular activity prediction"
    - "object oriented neural programming oonp for document understanding"

Connected through hyperedge HE-6 (induced by source paper "generative code modeling with graphs"):
  with members:
    - "learning deep generative models of graphs"
    - "designing random graph models using variational autoencoders with applications to chemical design"
    - "program synthesis using conflict driven learning"
    - "neural message passing for quantum chemistry"
    - "a survey of machine learning for big code and naturalness"
    - "abstract syntax networks for code generation and semantic parsing"
    - "learning to represent programs with graphs"
    - "a syntactic neural model for general purpose code generation"

Connected through hyperedge HE-7 (induced by source paper "deepbugs a learning approach to name based bug detection"):
  with members:
    - "a general path based representation for predicting program properties"
    - "end to end prediction of buffer overruns from raw source code via neural memory networks"
    - "smartpaste learning to adapt source code"
    - "a survey of machine learning for big code and naturalness"
    - "learning to represent programs with graphs"
    - "finding likely errors with bayesian specifications"
    - "learn fuzz machine learning for input fuzzing"
    - "automated correction for syntax errors in programming assignments using recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "deep learning to detect redundant method comments"):
  with members:
    - "a deep language model for software code"
    - "deep api learning"
    - "structured generative models of natural source code"

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)