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: "query2vec nlp meets databases for generalized workload analytics"
  Abstract: We propose methods for learning vector representations of SQL workloads to support a variety of administration tasks and application features, including query recommendation, workload summarization, index selection, identifying expensive queries, and predicting query reuse. We consider vector representations of both raw SQL text and optimized query plans under various assumptions and pre-processing strategies, and evaluate these methods on multiple real SQL workloads by comparing with results of task and application feature metrics in the literature. We find that simple algorithms based on these generic vector representations compete favorably with previous approaches that require a number of assumptions and task-specific heuristics. We then present a new embedding strategy specialized for queries based on tree-structured Long Short Term Memory (LSTM) network architectures that improves on the text-oriented embeddings for some tasks. We find that the general approach, when trained on a large corpus of SQL queries, provides a robust foundation for a variety of workload analysis tasks. We conclude by considering how workload embeddings can be deployed as a core database system feature to support database maintenance and novel applications.

Connected through hyperedge HE-1 (induced by source paper "facilitating sql query composition and analysis"):
  with members:
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a survey of machine learning for big code and naturalness"
    - "comparative study of cnn and rnn for natural language processing"
    - "recent trends in deep learning based natural language processing"
    - "convolutional neural networks for text categorization shallow word level vs deep character level"
    - "very deep convolutional networks for text classification"
    - "database meets deep learning challenges and opportunities"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-2 (induced by source paper "database agnostic workload management"):
  with members:
    - "comparative study of cnn and rnn for natural language processing"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "convolutional neural networks for sentence classification"
    - "recurrent neural network regularization"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

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.DB(Databases)