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: "model based approximate query processing"
  Abstract: Interactive visualizations are arguably the most important tool to explore, understand and convey facts about data. In the past years, the database community has been working on different techniques for Approximate Query Processing (AQP) that aim to deliver an approximate query result given a fixed time bound to support interactive visualizations better. However, classical AQP approaches suffer from various problems that limit the applicability to support the ad-hoc exploration of a new data set: (1) Classical AQP approaches that perform online sampling can support ad-hoc exploration queries but yield low quality if executed over rare subpopulations. (2) Classical AQP approaches that rely on offline sampling can use some form of biased sampling to mitigate these problems but require a priori knowledge of the workload, which is often not realistic if users want to explore a new database. In this paper, we present a new approach to AQP called Model-based AQP that leverages generative models learned over the complete database to answer SQL queries at interactive speeds. Different from classical AQP approaches, generative models allow us to compute responses to ad-hoc queries and deliver high-quality estimates also over rare subpopulations at the same time. In our experiments with real and synthetic data sets, we show that Model-based AQP can in many scenarios return more accurate results in a shorter runtime. Furthermore, we think that our techniques of using generative models presented in this paper can not only be used for AQP in databases but also has applications for other database problems including Query Optimization as well as Data Cleaning.

Connected through hyperedge HE-1 (induced by source paper "ml aqp query driven approximate query processing based on machine learning"):
  with members:
    - "approximate query processing using deep generative models"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "approximate query processing using deep generative models"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "idebench a benchmark for interactive data exploration"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "deep reinforcement learning for join order enumeration"
    - "data synthesis based on generative adversarial networks"
    - "learning to optimize join queries with deep reinforcement learning"
    - "neuralcubes deep representations for visual data exploration"
    - "verdictdb universalizing approximate query processing"

Connected through hyperedge HE-3 (induced by source paper "mosaic a sample based database system for open world query processing"):
  with members:
    - "approximate query processing using deep generative models"
    - "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"
    - "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-4 (induced by source paper "deepspace approximate geospatial query processing with deep learning"):
  with members:
    - "approximate query processing using deep generative models"
    - "selectivity estimation with deep likelihood models"
    - "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)