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: "a grammar based structural cnn decoder for code generation"
  Abstract: Code generation maps a program description to executable source code in a programming language. Existing approaches mainly rely on a recurrent neural network (RNN) as the decoder. However, we find that a program contains significantly more tokens than a natural language sentence, and thus it may be inappropriate for RNN to capture such a long sequence. In this paper, we propose a grammar-based structural convolutional neural network (CNN) for code generation. Our model generates a program by predicting the grammar rules of the programming language; we design several CNN modules, including the tree-based convolution and pre-order convolution, whose information is further aggregated by dedicated attentive pooling layers. Experimental results on the HearthStone benchmark dataset show that our CNN code generator significantly outperforms the previous state-of-the-art method by 5 percentage points; additional experiments on several semantic parsing tasks demonstrate the robustness of our model. We also conduct in-depth ablation test to better understand each component of our model.

Connected through hyperedge HE-1 (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"
    - "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"
    - "neural program meta induction"

Connected through hyperedge HE-2 (induced by source paper "code generation as a dual task of code summarization"):
  with members:
    - "dual ask answer network for machine reading comprehension"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "dual supervised learning"
    - "a parallel corpus of python functions and documentation strings for automated code documentation and code generation"
    - "abstract syntax networks for code generation and semantic parsing"
    - "question answering and question generation as dual tasks"
    - "a syntactic neural model for general purpose code generation"
    - "tree to sequence attentional neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "treegen a tree based transformer architecture for code generation"):
  with members:
    - "sequence to action end to end semantic graph generation for semantic parsing"
    - "abstract syntax networks for code generation and semantic parsing"
    - "deep residual learning for image recognition"

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.LG(Machine Learning)