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: "approximate query processing using deep generative models"
  Abstract: Data is generated at an unprecedented rate surpassing our ability to analyze them. The database community has pioneered many novel techniques for Approximate Query Processing (AQP) that could give approximate results in a fraction of time needed for computing exact results. In this work, we explore the usage of deep learning (DL) for answering aggregate queries specifically for interactive applications such as data exploration and visualization. We use deep generative models, an unsupervised learning based approach, to learn the data distribution faithfully such that aggregate queries could be answered approximately by generating samples from the learned model. The model is often compact - few hundred KBs - so that arbitrary AQP queries could be answered on the client side without contacting the database server. Our other contributions include identifying model bias and minimizing it through a rejection sampling based approach and an algorithm to build model ensembles for AQP for improved accuracy. Our extensive experiments show that our proposed approach can provide answers with high accuracy and low latency.

Connected through hyperedge HE-1 (induced by source paper "video monitoring queries"):
  with members:
    - "multi attribute selectivity estimation using deep learning"
    - "yolov3 an incremental improvement"
    - "blazeit fast exploratory video queries using neural networks"
    - "verdictdb universalizing approximate query processing"
    - "focus querying large video datasets with low latency and low cost"
    - "towards a hands free query optimizer through deep learning"
    - "the case for learned index structures"
    - "weakly supervised object localization with multi fold multiple instance learning"

Connected through hyperedge HE-2 (induced by source paper "ml aqp query driven approximate query processing based on machine learning"):
  with members:
    - "neo a learned query optimizer"
    - "selectivity estimation with deep likelihood models"
    - "deepdb learn from data not from queries"
    - "an empirical analysis of deep learning for cardinality estimation"
    - "an end to end learning based cost estimator"
    - "estimating cardinalities with deep sketches"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "neuralcubes deep representations for visual data exploration"

Connected through hyperedge HE-3 (induced by source paper "laqp learning based approximate query processing"):
  with members:
    - "diversity in machine learning"
    - "the case for learned index structures"

Connected through hyperedge HE-4 (induced by source paper "mosaic a sample based database system for open world query processing"):
  with members:
    - "neo a learned query optimizer"
    - "entropydb a probabilistic approach to approximate query processing"
    - "on constrained open world probabilistic databases"
    - "idebench a benchmark for interactive data exploration"
    - "model based approximate query processing"
    - "generative modeling using the sliced wasserstein distance"
    - "database learning toward a database that becomes smarter every time"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "deepdb learn from data not from queries"):
  with members:
    - "neo a learned query optimizer"
    - "spflow an easy and extensible library for deep probabilistic learning using sum product networks"
    - "multi attribute selectivity estimation using deep learning"
    - "an end to end learning based cost estimator"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "idebench a benchmark for interactive data exploration"
    - "verdictdb universalizing approximate query processing"
    - "the case for learned index structures"

Connected through hyperedge HE-6 (induced by source paper "entropydb a probabilistic approach to approximate query processing"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "verdictdb universalizing approximate query processing"
    - "probabilistic database summarization for interactive data exploration"
    - "generalization in adaptive data analysis and holdout reuse"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-7 (induced by source paper "deepspace approximate geospatial query processing with deep learning"):
  with members:
    - "selectivity estimation with deep likelihood models"
    - "model based approximate query processing"
    - "made masked autoencoder for distribution estimation"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

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)