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: "an encoder decoder framework translating natural language to database queries"
  Abstract: Machine translation is going through a radical revolution, driven by the explosive development of deep learning techniques using Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). In this paper, we consider a special case in machine translation problems, targeting to translate natural language into Structural Query Language (SQL) for data retrieval over relational database. Although generic CNN and RNN learn the grammar structure of SQL when trained with sufficient samples, the accuracy and training efficiency of the model could be dramatically improved, when the translation model is deeply integrated with the grammar rules of SQL. We present a new encoder-decoder framework, with a suite of new approaches, including new semantic features fed into the encoder as well as new grammar-aware states injected into the memory of decoder. These techniques help the neural network focus on understanding semantics of the operations in natural language and save the efforts on SQL grammar learning. The empirical evaluation on real world database and queries show that our approach outperform state-of-the-art solution by a significant margin.

Connected through hyperedge HE-1 (induced by source paper "zero shot text to sql learning with auxiliary task"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "natural language to structured query generation via meta learning"
    - "incsql training incremental text to sql parsers with non deterministic oracles"
    - "typesql knowledge based type aware neural text to sql generation"
    - "coarse to fine decoding for neural semantic parsing"

Connected through hyperedge HE-2 (induced by source paper "neural machine translating from natural language to sparql"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "semantic parsing natural language into sparql improving target language representation with neural attention"
    - "neural machine translation for query construction and composition"
    - "sparql as a foreign language"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "language to logical form with neural attention"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-3 (induced by source paper "bidirectional attention for sql generation"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "latent attention for if then program synthesis"
    - "language to logical form with neural attention"
    - "topic augmented neural network for short text conversation"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "database meets deep learning challenges and opportunities"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "deeper deep entity resolution"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "the case for learned index structures"
    - "device placement optimization with reinforcement learning"
    - "memory efficient implementation of densenets"
    - "learning a neural semantic parser from user feedback"
    - "revisiting distributed synchronous sgd"

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.CL(Computation and Language)